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chore: backup before code cleanup

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Backup commit before executing remove-unused-code proposal.
This includes all pending changes and new features.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
egg
2025-12-11 11:55:39 +08:00
parent eff9b0bcd5
commit 940a406dce
58 changed files with 8226 additions and 175 deletions
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23
.claude/commands/openspec/apply.md Normal file
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@@ -0,0 +1,23 @@
---
name: OpenSpec: Apply
description: Implement an approved OpenSpec change and keep tasks in sync.
category: OpenSpec
tags: [openspec, apply]
---
<!-- OPENSPEC:START -->
**Guardrails**
- Favor straightforward, minimal implementations first and add complexity only when it is requested or clearly required.
- Keep changes tightly scoped to the requested outcome.
- Refer to `openspec/AGENTS.md` (located inside the `openspec/` directory—run `ls openspec` or `openspec update` if you don't see it) if you need additional OpenSpec conventions or clarifications.
**Steps**
Track these steps as TODOs and complete them one by one.
1. Read `changes/<id>/proposal.md`, `design.md` (if present), and `tasks.md` to confirm scope and acceptance criteria.
2. Work through tasks sequentially, keeping edits minimal and focused on the requested change.
3. Confirm completion before updating statuses—make sure every item in `tasks.md` is finished.
4. Update the checklist after all work is done so each task is marked `- [x]` and reflects reality.
5. Reference `openspec list` or `openspec show <item>` when additional context is required.
**Reference**
- Use `openspec show <id> --json --deltas-only` if you need additional context from the proposal while implementing.
<!-- OPENSPEC:END -->

27
.claude/commands/openspec/archive.md Normal file
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@@ -0,0 +1,27 @@
---
name: OpenSpec: Archive
description: Archive a deployed OpenSpec change and update specs.
category: OpenSpec
tags: [openspec, archive]
---
<!-- OPENSPEC:START -->
**Guardrails**
- Favor straightforward, minimal implementations first and add complexity only when it is requested or clearly required.
- Keep changes tightly scoped to the requested outcome.
- Refer to `openspec/AGENTS.md` (located inside the `openspec/` directory—run `ls openspec` or `openspec update` if you don't see it) if you need additional OpenSpec conventions or clarifications.
**Steps**
1. Determine the change ID to archive:
- If this prompt already includes a specific change ID (for example inside a `<ChangeId>` block populated by slash-command arguments), use that value after trimming whitespace.
- If the conversation references a change loosely (for example by title or summary), run `openspec list` to surface likely IDs, share the relevant candidates, and confirm which one the user intends.
- Otherwise, review the conversation, run `openspec list`, and ask the user which change to archive; wait for a confirmed change ID before proceeding.
- If you still cannot identify a single change ID, stop and tell the user you cannot archive anything yet.
2. Validate the change ID by running `openspec list` (or `openspec show <id>`) and stop if the change is missing, already archived, or otherwise not ready to archive.
3. Run `openspec archive <id> --yes` so the CLI moves the change and applies spec updates without prompts (use `--skip-specs` only for tooling-only work).
4. Review the command output to confirm the target specs were updated and the change landed in `changes/archive/`.
5. Validate with `openspec validate --strict` and inspect with `openspec show <id>` if anything looks off.
**Reference**
- Use `openspec list` to confirm change IDs before archiving.
- Inspect refreshed specs with `openspec list --specs` and address any validation issues before handing off.
<!-- OPENSPEC:END -->

28
.claude/commands/openspec/proposal.md Normal file
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@@ -0,0 +1,28 @@
---
name: OpenSpec: Proposal
description: Scaffold a new OpenSpec change and validate strictly.
category: OpenSpec
tags: [openspec, change]
---
<!-- OPENSPEC:START -->
**Guardrails**
- Favor straightforward, minimal implementations first and add complexity only when it is requested or clearly required.
- Keep changes tightly scoped to the requested outcome.
- Refer to `openspec/AGENTS.md` (located inside the `openspec/` directory—run `ls openspec` or `openspec update` if you don't see it) if you need additional OpenSpec conventions or clarifications.
- Identify any vague or ambiguous details and ask the necessary follow-up questions before editing files.
- Do not write any code during the proposal stage. Only create design documents (proposal.md, tasks.md, design.md, and spec deltas). Implementation happens in the apply stage after approval.
**Steps**
1. Review `openspec/project.md`, run `openspec list` and `openspec list --specs`, and inspect related code or docs (e.g., via `rg`/`ls`) to ground the proposal in current behaviour; note any gaps that require clarification.
2. Choose a unique verb-led `change-id` and scaffold `proposal.md`, `tasks.md`, and `design.md` (when needed) under `openspec/changes/<id>/`.
3. Map the change into concrete capabilities or requirements, breaking multi-scope efforts into distinct spec deltas with clear relationships and sequencing.
4. Capture architectural reasoning in `design.md` when the solution spans multiple systems, introduces new patterns, or demands trade-off discussion before committing to specs.
5. Draft spec deltas in `changes/<id>/specs/<capability>/spec.md` (one folder per capability) using `## ADDED|MODIFIED|REMOVED Requirements` with at least one `#### Scenario:` per requirement and cross-reference related capabilities when relevant.
6. Draft `tasks.md` as an ordered list of small, verifiable work items that deliver user-visible progress, include validation (tests, tooling), and highlight dependencies or parallelizable work.
7. Validate with `openspec validate <id> --strict` and resolve every issue before sharing the proposal.
**Reference**
- Use `openspec show <id> --json --deltas-only` or `openspec show <spec> --type spec` to inspect details when validation fails.
- Search existing requirements with `rg -n "Requirement:|Scenario:" openspec/specs` before writing new ones.
- Explore the codebase with `rg <keyword>`, `ls`, or direct file reads so proposals align with current implementation realities.
<!-- OPENSPEC:END -->

53
backend/alembic/versions/f1a2b3c4d5e6_fix_sessions_schema_mismatch.py Normal file
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@@ -0,0 +1,53 @@
"""fix_sessions_schema_mismatch
Revision ID: f1a2b3c4d5e6
Revises: e51c9a16ee16
Create Date: 2025-12-10 10:30:00.000000
Fix schema mismatch between SQLAlchemy model and MySQL database:
1. Remove session_token column from tool_ocr_sessions (not in model)
2. Remove is_active column from tool_ocr_sessions (not in model)
3. Note: processing_track in tool_ocr_tasks is nullable so it won't cause issues
These columns were added to the database manually or by another migration
but are not present in the current SQLAlchemy models.
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import mysql
# revision identifiers, used by Alembic.
revision: str = 'f1a2b3c4d5e6'
down_revision: Union[str, None] = 'e51c9a16ee16'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""
Remove columns from tool_ocr_sessions that exist in DB but not in model.
This fixes the "Field 'session_token' doesn't have a default value" error.
"""
# Remove session_token column (exists in DB, not in model)
op.drop_column('tool_ocr_sessions', 'session_token')
# Remove is_active column (exists in DB, not in model)
op.drop_column('tool_ocr_sessions', 'is_active')
def downgrade() -> None:
"""
Re-add the columns if needed to rollback.
"""
# Re-add is_active column
op.add_column('tool_ocr_sessions',
sa.Column('is_active', mysql.TINYINT(), nullable=False, server_default='1',
comment='Whether the session is active'))
# Re-add session_token column
op.add_column('tool_ocr_sessions',
sa.Column('session_token', sa.String(length=255), nullable=False,
comment='Session token for authentication'))

146
backend/app/core/config.py
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@@ -97,9 +97,115 @@ class Settings(BaseSettings):
enable_region_detection: bool = Field(default=True) # Region detection for better table structure
enable_text_recognition: bool = Field(default=True) # General text recognition
# Table Parsing Mode - Controls how aggressively tables are parsed
# This is the KEY setting to prevent "cell explosion" on datasheet-type documents
# Options:
# - "full": Full table recognition with cell segmentation (aggressive, may over-detect)
# - "conservative": Conservative models + disable wireless tables + higher layout threshold
# - "classification_only": Only classify table regions, no cell segmentation (recommended for datasheets)
# - "disabled": Completely disable table recognition (safest for text-heavy documents)
table_parsing_mode: str = Field(
default="conservative",
description="Table parsing mode: 'full', 'conservative', 'classification_only', 'disabled'"
)
# Layout threshold for table detection (higher = stricter, less false positives)
# WARNING: This affects ALL layout detection, not just tables. Use with caution.
# Default None uses PaddleX default. Only set this if you understand the impact.
table_layout_threshold: Optional[float] = Field(
default=None,
description="Layout threshold for ALL element detection. Higher values = fewer elements detected."
)
# Cell Validation (filter over-detected table cells)
# DISABLED: This is a patch behavior - focus on getting PP-Structure output right first
cell_validation_enabled: bool = Field(
default=False,
description="Enable cell validation to filter over-detected tables"
)
cell_validation_max_density: float = Field(
default=3.0,
description="Max cells per 10,000px². Tables exceeding this are reclassified as TEXT."
)
cell_validation_min_cell_area: float = Field(
default=3000.0,
description="Min average cell area in px². Tables below this are reclassified as TEXT."
)
cell_validation_min_cell_height: float = Field(
default=10.0,
description="Min average cell height in px. Tables below this are reclassified as TEXT."
)
# Table Content Rebuilder (rebuild table HTML from raw OCR)
# DISABLED: This is a patch behavior - focus on getting PP-Structure output right first
table_content_rebuilder_enabled: bool = Field(
default=False,
description="Enable table content rebuilder to fix PP-Structure table HTML"
)
# Table Quality Check (determines rendering strategy based on cell_boxes overlap)
# When enabled, tables with overlapping cell_boxes are marked as 'bad' quality
# and rendered with border-only mode instead of full cell_boxes rendering.
# Disable this to always use cell_boxes rendering regardless of quality.
table_quality_check_enabled: bool = Field(
default=False,
description="Enable cell_boxes quality check. When disabled, all tables use cell_boxes rendering."
)
# Table Rendering: cell_boxes-first approach
# When enabled, uses cell_boxes coordinates as the primary source for table structure
# instead of relying on HTML colspan/rowspan, which often causes grid mismatch issues
# DISABLED: Algorithm needs improvement - clustering produces incorrect grid dimensions
table_rendering_prefer_cellboxes: bool = Field(
default=False,
description="Use cell_boxes coordinates as primary table structure source for PDF rendering"
)
table_cellboxes_row_threshold: float = Field(
default=15.0,
description="Y-coordinate threshold for row clustering when inferring grid from cell_boxes"
)
table_cellboxes_col_threshold: float = Field(
default=15.0,
description="X-coordinate threshold for column clustering when inferring grid from cell_boxes"
)
# Table Column Alignment Correction (Header-Anchor Algorithm)
# Corrects PP-Structure's column assignment errors using header row X-coordinates as reference
table_column_correction_enabled: bool = Field(
default=True,
description="Enable header-anchor column correction for table cells"
)
table_column_correction_threshold: float = Field(
default=0.5,
description="Minimum X-overlap ratio (0-1) to trigger column correction"
)
# Vertical Text Fragment Merging
# Detects and merges narrow vertical text blocks that were split by OCR
vertical_fragment_merge_enabled: bool = Field(
default=True,
description="Enable vertical text fragment merging for Chinese vertical text"
)
vertical_fragment_aspect_ratio: float = Field(
default=0.3,
description="Max width/height ratio to consider as vertical text (lower = narrower)"
)
# Simple Text Positioning Mode (OCR Track)
# When enabled, bypasses complex table structure reconstruction and renders
# raw OCR text directly at detected positions with rotation correction.
# This is more reliable for documents where PP-Structure fails to parse tables correctly.
simple_text_positioning_enabled: bool = Field(
default=True,
description="Use simple text positioning instead of complex table reconstruction for OCR track"
)
simple_text_positioning_debug: bool = Field(
default=False,
description="Enable debug logging for simple text positioning"
)
# PP-StructureV3 Preprocessing (Stage 1)
use_doc_orientation_classify: bool = Field(default=True) # Auto-detect and correct document rotation
use_doc_unwarping: bool = Field(default=True) # Correct document warping from photos
use_doc_unwarping: bool = Field(default=False) # Disabled: can cause document distortion/skewing
use_textline_orientation: bool = Field(default=True) # Detect textline orientation
# Layout Detection Parameters (Stage 3)
@@ -277,11 +383,43 @@ class Settings(BaseSettings):
# ===== Gap Filling Configuration =====
# Supplements PP-StructureV3 output with raw OCR regions when detection is incomplete
gap_filling_enabled: bool = Field(default=True) # Enable gap filling for OCR track
# Uses IoA (Intersection over Area) instead of IoU for better coverage detection
gap_filling_enabled: bool = Field(default=False) # Enable gap filling for OCR track
gap_filling_coverage_threshold: float = Field(default=0.7) # Activate when coverage < 70%
gap_filling_iou_threshold: float = Field(default=0.15) # IoU threshold for coverage detection
gap_filling_confidence_threshold: float = Field(default=0.3) # Min confidence for raw OCR regions
gap_filling_dedup_iou_threshold: float = Field(default=0.5) # IoU threshold for deduplication
# IoA (Intersection over Area) thresholds - different thresholds per element type
# IoA = intersection_area / ocr_box_area (measures how much of OCR box is inside layout region)
gap_filling_ioa_threshold_text: float = Field(
default=0.6,
description="IoA threshold for TEXT/TITLE elements. Tolerates boundary errors."
)
gap_filling_ioa_threshold_table: float = Field(
default=0.1,
description="IoA threshold for TABLE elements. Strict to prevent duplicate table content."
)
gap_filling_ioa_threshold_figure: float = Field(
default=0.8,
description="IoA threshold for FIGURE/IMAGE elements. Preserves text inside figures."
)
gap_filling_dedup_ioa_threshold: float = Field(
default=0.5,
description="IoA threshold for deduplication against existing TEXT elements."
)
gap_filling_shrink_pixels: int = Field(
default=1,
description="Shrink OCR bbox inward by this many pixels to reduce edge duplicates."
)
# Use PP-StructureV3's internal OCR (overall_ocr_res) instead of separate Raw OCR
gap_filling_use_overall_ocr: bool = Field(
default=True,
description="Use PP-StructureV3's internal OCR results instead of separate inference."
)
# Legacy IoU threshold (deprecated, kept for backward compatibility)
gap_filling_iou_threshold: float = Field(default=0.15) # Deprecated: use IoA thresholds
gap_filling_dedup_iou_threshold: float = Field(default=0.5) # Deprecated: use gap_filling_dedup_ioa_threshold
# ===== Debug Configuration =====
# Enable debug outputs for PP-StructureV3 analysis

48
backend/app/routers/tasks.py
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@@ -41,6 +41,9 @@ from app.schemas.task import (
PreprocessingPreviewResponse,
ImageQualityMetrics,
TableDetectionConfig,
OCRPresetEnum,
OCRConfig,
OCR_PRESET_CONFIGS,
)
from app.services.task_service import task_service
from app.services.file_access_service import file_access_service
@@ -77,7 +80,9 @@ def process_task_ocr(
layout_model: Optional[str] = "chinese",
preprocessing_mode: Optional[str] = "auto",
preprocessing_config: Optional[dict] = None,
table_detection_config: Optional[dict] = None
table_detection_config: Optional[dict] = None,
ocr_preset: Optional[str] = None,
ocr_config: Optional[dict] = None
):
"""
Background task to process OCR for a task with dual-track support.
@@ -97,6 +102,8 @@ def process_task_ocr(
preprocessing_mode: Preprocessing mode ('auto', 'manual', 'disabled')
preprocessing_config: Manual preprocessing config dict (contrast, sharpen, binarize)
table_detection_config: Table detection config dict (enable_wired_table, enable_wireless_table, enable_region_detection)
ocr_preset: OCR processing preset (text_heavy, datasheet, table_heavy, form, mixed, custom)
ocr_config: Custom OCR config dict (overrides preset values)
"""
from app.core.database import SessionLocal
from app.models.task import Task
@@ -135,6 +142,26 @@ def process_task_ocr(
enable_region_detection=table_detection_config.get("enable_region_detection", True)
)
# Convert OCR preset and config to proper objects
from app.schemas.task import OCRPresetEnum, OCRConfig, OCR_PRESET_CONFIGS, TableParsingModeEnum
ocr_config_obj = None
if ocr_preset:
preset_enum = OCRPresetEnum(ocr_preset)
# Get preset config as base
if preset_enum in OCR_PRESET_CONFIGS:
ocr_config_obj = OCR_PRESET_CONFIGS[preset_enum].model_copy()
else:
# CUSTOM preset - use provided config or defaults
ocr_config_obj = OCRConfig()
# Override with custom config values if provided
if ocr_config:
for key, value in ocr_config.items():
if hasattr(ocr_config_obj, key) and value is not None:
setattr(ocr_config_obj, key, value)
logger.info(f"OCR config resolved: preset={ocr_preset}, config={ocr_config_obj.model_dump() if ocr_config_obj else None}")
# Get task directly by database ID (bypass user isolation for background task)
task = db.query(Task).filter(Task.id == task_db_id).first()
if not task:
@@ -184,7 +211,8 @@ def process_task_ocr(
layout_model=layout_model,
preprocessing_mode=preprocess_mode_enum,
preprocessing_config=preprocess_config_obj,
table_detection_config=table_det_config_obj
table_detection_config=table_det_config_obj,
ocr_config=ocr_config_obj
)
else:
# Fall back to traditional processing (no force_track support)
@@ -196,7 +224,8 @@ def process_task_ocr(
layout_model=layout_model,
preprocessing_mode=preprocess_mode_enum,
preprocessing_config=preprocess_config_obj,
table_detection_config=table_det_config_obj
table_detection_config=table_det_config_obj,
ocr_config=ocr_config_obj
)
# Calculate processing time
@@ -827,6 +856,13 @@ async def start_task(
}
logger.info(f"Table detection: {table_detection_config}")
# Extract OCR preset and config
ocr_preset = options.ocr_preset.value if options.ocr_preset else "datasheet"
ocr_config_dict = None
if options.ocr_config:
ocr_config_dict = options.ocr_config.model_dump()
logger.info(f"OCR preset: {ocr_preset}, config: {ocr_config_dict}")
# Get task details
task = task_service.get_task_by_id(
db=db,
@@ -876,11 +912,13 @@ async def start_task(
layout_model=layout_model,
preprocessing_mode=preprocessing_mode,
preprocessing_config=preprocessing_config,
table_detection_config=table_detection_config
table_detection_config=table_detection_config,
ocr_preset=ocr_preset,
ocr_config=ocr_config_dict
)
logger.info(f"Started OCR processing task {task_id} for user {current_user.email}")
logger.info(f"Options: dual_track={use_dual_track}, force_track={force_track}, lang={language}, layout_model={layout_model}, preprocessing={preprocessing_mode}, table_detection={table_detection_config}")
logger.info(f"Options: dual_track={use_dual_track}, force_track={force_track}, lang={language}, layout_model={layout_model}, preprocessing={preprocessing_mode}, table_detection={table_detection_config}, ocr_preset={ocr_preset}")
return task
except HTTPException:

144
backend/app/schemas/task.py
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@@ -65,6 +65,139 @@ class PreprocessingContrastEnum(str, Enum):
DOCUMENT = "document"
class OCRPresetEnum(str, Enum):
"""OCR processing preset for different document types.
Presets provide optimized PP-Structure configurations for common document types:
- TEXT_HEAVY: Reports, articles, manuals (disable table recognition)
- DATASHEET: Technical datasheets, TDS (conservative table parsing)
- TABLE_HEAVY: Financial reports, spreadsheets (full table recognition)
- FORM: Applications, surveys (conservative table parsing)
- MIXED: General documents (classification only)
- CUSTOM: User-defined settings (use ocr_config)
"""
TEXT_HEAVY = "text_heavy" # Reports, articles, manuals
DATASHEET = "datasheet" # Technical datasheets, TDS
TABLE_HEAVY = "table_heavy" # Financial reports, spreadsheets
FORM = "form" # Applications, surveys
MIXED = "mixed" # General documents
CUSTOM = "custom" # User-defined settings
class TableParsingModeEnum(str, Enum):
"""Table parsing mode controlling how aggressively tables are parsed.
- FULL: Full table recognition with cell segmentation (aggressive)
- CONSERVATIVE: Disable wireless tables to prevent cell explosion
- CLASSIFICATION_ONLY: Only classify table regions, no cell segmentation
- DISABLED: Completely disable table recognition
"""
FULL = "full"
CONSERVATIVE = "conservative"
CLASSIFICATION_ONLY = "classification_only"
DISABLED = "disabled"
class OCRConfig(BaseModel):
"""OCR processing configuration for PP-Structure.
Allows fine-grained control over PP-Structure parameters.
Use with ocr_preset=CUSTOM or to override specific preset values.
"""
# Table Processing
table_parsing_mode: TableParsingModeEnum = Field(
default=TableParsingModeEnum.CONSERVATIVE,
description="Table parsing mode: full, conservative, classification_only, disabled"
)
enable_wired_table: bool = Field(
default=True,
description="Enable wired (bordered) table detection"
)
enable_wireless_table: bool = Field(
default=False,
description="Enable wireless (borderless) table detection. Can cause cell explosion."
)
# Layout Detection
layout_threshold: Optional[float] = Field(
default=None,
ge=0.0,
le=1.0,
description="Layout detection threshold. Higher = stricter. None uses default."
)
layout_nms_threshold: Optional[float] = Field(
default=None,
ge=0.0,
le=1.0,
description="Layout NMS threshold. None uses default."
)
# Preprocessing
use_doc_orientation_classify: bool = Field(
default=True,
description="Auto-detect and correct document rotation"
)
use_doc_unwarping: bool = Field(
default=False,
description="Correct document warping. Can cause distortion."
)
use_textline_orientation: bool = Field(
default=True,
description="Detect textline orientation"
)
# Recognition Modules
enable_chart_recognition: bool = Field(
default=True,
description="Enable chart/diagram recognition"
)
enable_formula_recognition: bool = Field(
default=True,
description="Enable math formula recognition"
)
enable_seal_recognition: bool = Field(
default=False,
description="Enable seal/stamp recognition"
)
enable_region_detection: bool = Field(
default=True,
description="Enable region detection for better structure"
)
# Preset configurations mapping
OCR_PRESET_CONFIGS = {
OCRPresetEnum.TEXT_HEAVY: OCRConfig(
table_parsing_mode=TableParsingModeEnum.DISABLED,
enable_wired_table=False,
enable_wireless_table=False,
enable_chart_recognition=False,
enable_formula_recognition=False,
),
OCRPresetEnum.DATASHEET: OCRConfig(
table_parsing_mode=TableParsingModeEnum.CONSERVATIVE,
enable_wired_table=True,
enable_wireless_table=False,
),
OCRPresetEnum.TABLE_HEAVY: OCRConfig(
table_parsing_mode=TableParsingModeEnum.FULL,
enable_wired_table=True,
enable_wireless_table=True,
),
OCRPresetEnum.FORM: OCRConfig(
table_parsing_mode=TableParsingModeEnum.CONSERVATIVE,
enable_wired_table=True,
enable_wireless_table=False,
),
OCRPresetEnum.MIXED: OCRConfig(
table_parsing_mode=TableParsingModeEnum.CLASSIFICATION_ONLY,
enable_wired_table=True,
enable_wireless_table=False,
),
# CUSTOM uses user-provided config directly
}
class PreprocessingConfig(BaseModel):
"""Preprocessing configuration for layout detection enhancement.
@@ -329,6 +462,17 @@ class ProcessingOptions(BaseModel):
description="Table detection config. If None, all table detection modes are enabled."
)
# OCR Processing Preset (OCR track only)
# Use presets for optimized configurations or CUSTOM with ocr_config for fine-tuning
ocr_preset: Optional[OCRPresetEnum] = Field(
default=OCRPresetEnum.DATASHEET,
description="OCR processing preset: text_heavy, datasheet, table_heavy, form, mixed, custom"
)
ocr_config: Optional[OCRConfig] = Field(
None,
description="Custom OCR config. Used when ocr_preset=custom or to override preset values."
)
class AnalyzeRequest(BaseModel):
"""Document analysis request"""

583
backend/app/services/cell_validation_engine.py Normal file
View File

@@ -0,0 +1,583 @@
"""
Cell Validation Engine
Validates PP-StructureV3 table detections using metric-based heuristics
to filter over-detected cells and reclassify invalid tables as TEXT elements.
Metrics used:
- Cell density: cells per 10,000 px² (normal: 0.4-1.0, over-detected: 6+)
- Average cell area: px² per cell (normal: 10,000-25,000, over-detected: ~1,600)
- Cell height: table_height / cell_count (minimum: 10px for readable text)
"""
import logging
from dataclasses import dataclass
from typing import List, Dict, Any, Optional, Tuple
from html.parser import HTMLParser
import re
logger = logging.getLogger(__name__)
@dataclass
class CellValidationConfig:
"""Configuration for cell validation thresholds."""
max_cell_density: float = 3.0 # cells per 10,000 px²
min_avg_cell_area: float = 3000.0 # px² per cell
min_cell_height: float = 10.0 # px per cell row
enabled: bool = True
@dataclass
class TableValidationResult:
"""Result of table validation."""
is_valid: bool
table_element: Dict[str, Any]
reason: Optional[str] = None
metrics: Optional[Dict[str, float]] = None
class CellValidationEngine:
"""
Validates table elements from PP-StructureV3 output.
Over-detected tables are identified by abnormal metrics and
reclassified as TEXT elements while preserving content.
"""
def __init__(self, config: Optional[CellValidationConfig] = None):
self.config = config or CellValidationConfig()
def calculate_table_metrics(
self,
bbox: List[float],
cell_boxes: List[List[float]]
) -> Dict[str, float]:
"""
Calculate validation metrics for a table.
Args:
bbox: Table bounding box [x0, y0, x1, y1]
cell_boxes: List of cell bounding boxes
Returns:
Dictionary with calculated metrics
"""
if len(bbox) < 4:
return {"cell_count": 0, "cell_density": 0, "avg_cell_area": 0, "avg_cell_height": 0}
cell_count = len(cell_boxes)
if cell_count == 0:
return {"cell_count": 0, "cell_density": 0, "avg_cell_area": 0, "avg_cell_height": 0}
# Calculate table dimensions
table_width = bbox[2] - bbox[0]
table_height = bbox[3] - bbox[1]
table_area = table_width * table_height
if table_area <= 0:
return {"cell_count": cell_count, "cell_density": 0, "avg_cell_area": 0, "avg_cell_height": 0}
# Cell density: cells per 10,000 px²
cell_density = (cell_count / table_area) * 10000
# Average cell area
avg_cell_area = table_area / cell_count
# Average cell height (table height / cell count)
avg_cell_height = table_height / cell_count
return {
"cell_count": cell_count,
"table_width": table_width,
"table_height": table_height,
"table_area": table_area,
"cell_density": cell_density,
"avg_cell_area": avg_cell_area,
"avg_cell_height": avg_cell_height
}
def validate_table(
self,
element: Dict[str, Any]
) -> TableValidationResult:
"""
Validate a single table element.
Args:
element: Table element from PP-StructureV3 output
Returns:
TableValidationResult with validation status and metrics
"""
if not self.config.enabled:
return TableValidationResult(is_valid=True, table_element=element)
# Extract bbox and cell_boxes
bbox = element.get("bbox", [])
cell_boxes = element.get("cell_boxes", [])
# Tables without cells pass validation (structure-only tables)
if not cell_boxes:
return TableValidationResult(
is_valid=True,
table_element=element,
reason="No cells to validate"
)
# Calculate metrics
metrics = self.calculate_table_metrics(bbox, cell_boxes)
# Check cell density
if metrics["cell_density"] > self.config.max_cell_density:
return TableValidationResult(
is_valid=False,
table_element=element,
reason=f"Cell density {metrics['cell_density']:.2f} exceeds threshold {self.config.max_cell_density}",
metrics=metrics
)
# Check average cell area
if metrics["avg_cell_area"] < self.config.min_avg_cell_area:
return TableValidationResult(
is_valid=False,
table_element=element,
reason=f"Avg cell area {metrics['avg_cell_area']:.0f}px² below threshold {self.config.min_avg_cell_area}px²",
metrics=metrics
)
# Check cell height
if metrics["avg_cell_height"] < self.config.min_cell_height:
return TableValidationResult(
is_valid=False,
table_element=element,
reason=f"Avg cell height {metrics['avg_cell_height']:.1f}px below threshold {self.config.min_cell_height}px",
metrics=metrics
)
# Content-based validation: check if content looks like prose vs tabular data
content_check = self._validate_table_content(element)
if not content_check["is_tabular"]:
return TableValidationResult(
is_valid=False,
table_element=element,
reason=content_check["reason"],
metrics=metrics
)
return TableValidationResult(
is_valid=True,
table_element=element,
metrics=metrics
)
def _validate_table_content(self, element: Dict[str, Any]) -> Dict[str, Any]:
"""
Validate table content to detect false positive tables.
Checks:
1. Sparsity: text coverage ratio (text area / table area)
2. Header: does table have proper header structure
3. Key-Value: for 2-col tables, is it a key-value list or random layout
4. Prose: are cells containing long prose text
Returns:
Dict with is_tabular (bool) and reason (str)
"""
html_content = element.get("content", "")
bbox = element.get("bbox", [])
cell_boxes = element.get("cell_boxes", [])
if not html_content or '<table' not in html_content.lower():
return {"is_tabular": True, "reason": "no_html_content"}
try:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')
table = soup.find('table')
if not table:
return {"is_tabular": True, "reason": "no_table_element"}
rows = table.find_all('tr')
if not rows:
return {"is_tabular": True, "reason": "no_rows"}
# Extract cell contents with row structure
row_data = []
all_cells = []
for row_idx, row in enumerate(rows):
cells = row.find_all(['td', 'th'])
row_cells = []
for cell in cells:
text = cell.get_text(strip=True)
colspan = int(cell.get('colspan', 1))
is_header = cell.name == 'th'
cell_info = {
"text": text,
"length": len(text),
"colspan": colspan,
"is_header": is_header,
"row": row_idx
}
row_cells.append(cell_info)
all_cells.append(cell_info)
row_data.append(row_cells)
if not all_cells:
return {"is_tabular": True, "reason": "no_cells"}
num_rows = len(row_data)
num_cols = max(len(r) for r in row_data) if row_data else 0
# === Check 1: Sparsity (text coverage) ===
sparsity_result = self._check_sparsity(bbox, cell_boxes, all_cells)
if not sparsity_result["is_valid"]:
return {"is_tabular": False, "reason": sparsity_result["reason"]}
# === Check 2: Header structure ===
header_result = self._check_header_structure(row_data, num_cols)
if not header_result["has_header"] and num_rows > 3:
# Large table without header is suspicious
logger.debug(f"Table has no header structure with {num_rows} rows")
# === Check 3: Key-Value pattern for 2-column tables ===
if num_cols == 2:
kv_result = self._check_key_value_pattern(row_data)
if kv_result["is_kv_list"] and kv_result["confidence"] > 0.7:
# High confidence key-value list - keep as table but log
logger.debug(f"Table identified as key-value list (conf={kv_result['confidence']:.2f})")
elif not kv_result["is_kv_list"] and kv_result["is_random_layout"]:
# Random 2-column layout, not a real table
return {
"is_tabular": False,
"reason": f"random_two_column_layout (not key-value)"
}
# === Check 4: Prose content ===
long_cells = [c for c in all_cells if c["length"] > 80]
prose_ratio = len(long_cells) / len(all_cells) if all_cells else 0
if prose_ratio > 0.3:
return {
"is_tabular": False,
"reason": f"prose_content ({len(long_cells)}/{len(all_cells)} cells > 80 chars)"
}
# === Check 5: Section header as table ===
if num_rows <= 2 and num_cols <= 2:
first_row = row_data[0] if row_data else []
if len(first_row) == 1:
text = first_row[0]["text"]
if text.isupper() and len(text) < 50:
return {
"is_tabular": False,
"reason": f"section_header_only ({text[:30]})"
}
return {"is_tabular": True, "reason": "content_valid"}
except Exception as e:
logger.warning(f"Content validation failed: {e}")
return {"is_tabular": True, "reason": f"validation_error: {e}"}
def _check_sparsity(
self,
bbox: List[float],
cell_boxes: List[List[float]],
all_cells: List[Dict]
) -> Dict[str, Any]:
"""
Check text coverage ratio (sparsity).
Two-column layouts have large empty gaps in the middle.
Real tables have more uniform cell distribution.
"""
if len(bbox) < 4:
return {"is_valid": True, "reason": "no_bbox"}
table_width = bbox[2] - bbox[0]
table_height = bbox[3] - bbox[1]
table_area = table_width * table_height
if table_area <= 0:
return {"is_valid": True, "reason": "invalid_area"}
# Calculate text area from cell_boxes
if cell_boxes:
text_area = 0
for cb in cell_boxes:
if len(cb) >= 4:
w = abs(cb[2] - cb[0])
h = abs(cb[3] - cb[1])
text_area += w * h
coverage = text_area / table_area
else:
# Estimate from cell content length
total_chars = sum(c["length"] for c in all_cells)
# Rough estimate: 1 char ≈ 8x12 pixels = 96 px²
estimated_text_area = total_chars * 96
coverage = min(estimated_text_area / table_area, 1.0)
# Very sparse table (< 15% coverage) is suspicious
if coverage < 0.15:
return {
"is_valid": False,
"reason": f"sparse_content (coverage={coverage:.1%})"
}
return {"is_valid": True, "coverage": coverage}
def _check_header_structure(
self,
row_data: List[List[Dict]],
num_cols: int
) -> Dict[str, Any]:
"""
Check if table has proper header structure.
Real tables usually have:
- First row with <th> elements
- Or first row with different content pattern (labels vs values)
"""
if not row_data:
return {"has_header": False}
first_row = row_data[0]
# Check for <th> elements
th_count = sum(1 for c in first_row if c.get("is_header", False))
if th_count > 0 and th_count >= len(first_row) * 0.5:
return {"has_header": True, "type": "th_elements"}
# Check for header-like content (short, distinct from body)
if len(row_data) > 1:
first_row_avg_len = sum(c["length"] for c in first_row) / len(first_row) if first_row else 0
body_rows = row_data[1:]
body_cells = [c for row in body_rows for c in row]
body_avg_len = sum(c["length"] for c in body_cells) / len(body_cells) if body_cells else 0
# Header row should be shorter (labels) than body (data)
if first_row_avg_len < body_avg_len * 0.7:
return {"has_header": True, "type": "short_labels"}
return {"has_header": False}
def _check_key_value_pattern(
self,
row_data: List[List[Dict]]
) -> Dict[str, Any]:
"""
For 2-column tables, check if it's a key-value list.
Key-value characteristics:
- Left column: short labels (< 30 chars)
- Right column: values (can be longer)
- Consistent pattern across rows
Random layout characteristics:
- Both columns have similar length distribution
- No clear label-value relationship
"""
if not row_data:
return {"is_kv_list": False, "is_random_layout": False, "confidence": 0}
left_lengths = []
right_lengths = []
kv_rows = 0
total_rows = 0
for row in row_data:
if len(row) != 2:
continue
total_rows += 1
left = row[0]
right = row[1]
left_lengths.append(left["length"])
right_lengths.append(right["length"])
# Key-value pattern: left is short label, right is value
if left["length"] < 40 and left["length"] < right["length"] * 2:
kv_rows += 1
if total_rows == 0:
return {"is_kv_list": False, "is_random_layout": False, "confidence": 0}
kv_ratio = kv_rows / total_rows
avg_left = sum(left_lengths) / len(left_lengths) if left_lengths else 0
avg_right = sum(right_lengths) / len(right_lengths) if right_lengths else 0
# High KV ratio and left column is shorter = key-value list
if kv_ratio > 0.6 and avg_left < avg_right:
return {
"is_kv_list": True,
"is_random_layout": False,
"confidence": kv_ratio,
"avg_left": avg_left,
"avg_right": avg_right
}
# Similar lengths on both sides = random layout
if avg_left > 0 and 0.5 < avg_right / avg_left < 2.0:
# Both columns have similar content length
return {
"is_kv_list": False,
"is_random_layout": True,
"confidence": 1 - kv_ratio,
"avg_left": avg_left,
"avg_right": avg_right
}
return {
"is_kv_list": False,
"is_random_layout": False,
"confidence": 0,
"avg_left": avg_left,
"avg_right": avg_right
}
def extract_text_from_table_html(self, html_content: str) -> str:
"""
Extract plain text from table HTML content.
Args:
html_content: HTML string containing table structure
Returns:
Plain text extracted from table cells
"""
if not html_content:
return ""
try:
class TableTextExtractor(HTMLParser):
def __init__(self):
super().__init__()
self.text_parts = []
self.in_cell = False
def handle_starttag(self, tag, attrs):
if tag in ('td', 'th'):
self.in_cell = True
def handle_endtag(self, tag):
if tag in ('td', 'th'):
self.in_cell = False
def handle_data(self, data):
if self.in_cell:
stripped = data.strip()
if stripped:
self.text_parts.append(stripped)
parser = TableTextExtractor()
parser.feed(html_content)
return ' '.join(parser.text_parts)
except Exception as e:
logger.warning(f"Failed to parse table HTML: {e}")
# Fallback: strip HTML tags with regex
text = re.sub(r'<[^>]+>', ' ', html_content)
text = re.sub(r'\s+', ' ', text).strip()
return text
def reclassify_as_text(self, element: Dict[str, Any]) -> Dict[str, Any]:
"""
Convert an over-detected table element to a TEXT element.
Args:
element: Table element to reclassify
Returns:
New TEXT element with preserved content
"""
# Extract text content from HTML
html_content = element.get("content", "")
text_content = self.extract_text_from_table_html(html_content)
# Create new TEXT element
text_element = {
"element_id": element.get("element_id", ""),
"type": "text",
"original_type": "table_reclassified", # Mark as reclassified
"content": text_content,
"page": element.get("page", 0),
"bbox": element.get("bbox", []),
"index": element.get("index", 0),
"confidence": element.get("confidence", 1.0),
"reclassified_from": "table",
"reclassification_reason": "over_detection"
}
return text_element
def validate_and_filter_elements(
self,
elements: List[Dict[str, Any]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
"""
Validate all elements and filter/reclassify over-detected tables.
Args:
elements: List of elements from PP-StructureV3 output
Returns:
Tuple of (filtered_elements, statistics)
"""
filtered_elements = []
stats = {
"total_tables": 0,
"valid_tables": 0,
"reclassified_tables": 0,
"reclassification_details": []
}
for element in elements:
if element.get("type") != "table":
# Non-table elements pass through unchanged
filtered_elements.append(element)
continue
stats["total_tables"] += 1
# Validate table
result = self.validate_table(element)
if result.is_valid:
stats["valid_tables"] += 1
filtered_elements.append(element)
else:
# Reclassify as TEXT
stats["reclassified_tables"] += 1
text_element = self.reclassify_as_text(element)
filtered_elements.append(text_element)
stats["reclassification_details"].append({
"element_id": element.get("element_id"),
"reason": result.reason,
"metrics": result.metrics
})
logger.info(
f"Reclassified table {element.get('element_id')} as TEXT: {result.reason}"
)
# Re-sort by reading order (y0 then x0)
filtered_elements = self._sort_by_reading_order(filtered_elements)
return filtered_elements, stats
def _sort_by_reading_order(
self,
elements: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
"""Sort elements by reading order (top-to-bottom, left-to-right)."""
def sort_key(elem):
bbox = elem.get("bbox", [0, 0, 0, 0])
if isinstance(bbox, dict):
y0 = bbox.get("y0", 0)
x0 = bbox.get("x0", 0)
elif isinstance(bbox, list) and len(bbox) >= 2:
x0, y0 = bbox[0], bbox[1]
else:
y0, x0 = 0, 0
return (y0, x0)
return sorted(elements, key=sort_key)

217
backend/app/services/gap_filling_service.py
View File

@@ -83,12 +83,34 @@ class TextRegion:
return ((x0 + x1) / 2, (y0 + y1) / 2)
# Element type to IoA threshold mapping
# TABLE needs strict filtering (low threshold) to prevent duplicate content
# FIGURE allows more text through (high threshold) to preserve axis labels, legends
# TEXT/TITLE uses moderate threshold to tolerate boundary detection errors
ELEMENT_TYPE_IOA_THRESHOLDS = {
ElementType.TABLE: 'table',
ElementType.FIGURE: 'figure',
ElementType.IMAGE: 'figure',
ElementType.CHART: 'figure',
ElementType.DIAGRAM: 'figure',
}
class GapFillingService:
"""
Service for detecting and filling gaps in PP-StructureV3 output.
This service uses IoA (Intersection over Area) algorithm for coverage detection,
which correctly measures "small box contained in large box" relationship.
Key improvements over IoU:
- IoA = intersection_area / ocr_box_area (non-symmetric)
- Better for detecting if OCR text is covered by larger layout regions
- Different thresholds per element type (TEXT, TABLE, FIGURE)
- Optional boundary shrinking to reduce edge duplicates
This service:
1. Calculates coverage of PP-StructureV3 elements over raw OCR regions
1. Calculates coverage of PP-StructureV3 elements over raw OCR regions using IoA
2. Identifies uncovered raw OCR regions
3. Supplements uncovered regions as TEXT elements
4. Deduplicates against existing PP-StructureV3 TEXT elements
@@ -98,9 +120,12 @@ class GapFillingService:
def __init__(
self,
coverage_threshold: float = None,
iou_threshold: float = None,
confidence_threshold: float = None,
dedup_iou_threshold: float = None,
ioa_threshold_text: float = None,
ioa_threshold_table: float = None,
ioa_threshold_figure: float = None,
dedup_ioa_threshold: float = None,
shrink_pixels: int = None,
enabled: bool = None
):
"""
@@ -108,27 +133,48 @@ class GapFillingService:
Args:
coverage_threshold: Coverage ratio below which gap filling activates (default: 0.7)
iou_threshold: IoU threshold for coverage detection (default: 0.15)
confidence_threshold: Minimum confidence for raw OCR regions (default: 0.3)
dedup_iou_threshold: IoU threshold for deduplication (default: 0.5)
ioa_threshold_text: IoA threshold for TEXT/TITLE elements (default: 0.6)
ioa_threshold_table: IoA threshold for TABLE elements (default: 0.1)
ioa_threshold_figure: IoA threshold for FIGURE/IMAGE elements (default: 0.8)
dedup_ioa_threshold: IoA threshold for deduplication (default: 0.5)
shrink_pixels: Shrink OCR bbox inward by this many pixels (default: 1)
enabled: Whether gap filling is enabled (default: True)
"""
self.coverage_threshold = coverage_threshold if coverage_threshold is not None else getattr(
settings, 'gap_filling_coverage_threshold', 0.7
)
self.iou_threshold = iou_threshold if iou_threshold is not None else getattr(
settings, 'gap_filling_iou_threshold', 0.15
)
self.confidence_threshold = confidence_threshold if confidence_threshold is not None else getattr(
settings, 'gap_filling_confidence_threshold', 0.3
)
self.dedup_iou_threshold = dedup_iou_threshold if dedup_iou_threshold is not None else getattr(
settings, 'gap_filling_dedup_iou_threshold', 0.5
# IoA thresholds per element type
self.ioa_threshold_text = ioa_threshold_text if ioa_threshold_text is not None else getattr(
settings, 'gap_filling_ioa_threshold_text', 0.6
)
self.ioa_threshold_table = ioa_threshold_table if ioa_threshold_table is not None else getattr(
settings, 'gap_filling_ioa_threshold_table', 0.1
)
self.ioa_threshold_figure = ioa_threshold_figure if ioa_threshold_figure is not None else getattr(
settings, 'gap_filling_ioa_threshold_figure', 0.8
)
self.dedup_ioa_threshold = dedup_ioa_threshold if dedup_ioa_threshold is not None else getattr(
settings, 'gap_filling_dedup_ioa_threshold', 0.5
)
# Boundary shrinking
self.shrink_pixels = shrink_pixels if shrink_pixels is not None else getattr(
settings, 'gap_filling_shrink_pixels', 1
)
self.enabled = enabled if enabled is not None else getattr(
settings, 'gap_filling_enabled', True
)
# Legacy compatibility
self.iou_threshold = getattr(settings, 'gap_filling_iou_threshold', 0.15)
self.dedup_iou_threshold = getattr(settings, 'gap_filling_dedup_iou_threshold', 0.5)
def should_activate(
self,
raw_ocr_regions: List[TextRegion],
@@ -209,21 +255,83 @@ class GapFillingService:
logger.debug(f"Found {len(uncovered)} uncovered regions out of {len(raw_ocr_regions)}")
return uncovered
def _get_ioa_threshold_for_element(self, element_type: ElementType) -> float:
"""
Get the IoA threshold for a specific element type.
Different element types have different thresholds:
- TABLE: 0.1 (strict, prevents duplicate table content)
- FIGURE/IMAGE: 0.8 (preserves text inside figures)
- TEXT/others: 0.6 (tolerates boundary errors)
Args:
element_type: The element type to get threshold for
Returns:
IoA threshold value
"""
threshold_type = ELEMENT_TYPE_IOA_THRESHOLDS.get(element_type, 'text')
if threshold_type == 'table':
return self.ioa_threshold_table
elif threshold_type == 'figure':
return self.ioa_threshold_figure
else:
return self.ioa_threshold_text
def _shrink_bbox(
self,
bbox: Tuple[float, float, float, float],
pixels: int
) -> Tuple[float, float, float, float]:
"""
Shrink a bounding box inward by the specified number of pixels.
This reduces false "uncovered" detection at region boundaries.
Args:
bbox: Original bbox (x0, y0, x1, y1)
pixels: Number of pixels to shrink on each side
Returns:
Shrunk bbox (x0, y0, x1, y1)
"""
x0, y0, x1, y1 = bbox
# Ensure we don't shrink to negative width/height
width = x1 - x0
height = y1 - y0
max_shrink = min(width / 2, height / 2, pixels)
return (
x0 + max_shrink,
y0 + max_shrink,
x1 - max_shrink,
y1 - max_shrink
)
def _is_region_covered(
self,
region: TextRegion,
pp_structure_elements: List[DocumentElement],
skip_table_coverage: bool = True
skip_table_coverage: bool = False
) -> bool:
"""
Check if a raw OCR region is covered by any PP-StructureV3 element.
Uses IoA (Intersection over Area) instead of IoU for better coverage detection.
IoA = intersection_area / ocr_box_area
This correctly measures "OCR box is contained in layout region".
Different element types use different IoA thresholds:
- TABLE: 0.1 (strict, any overlap means covered)
- FIGURE/IMAGE: 0.8 (preserve text inside figures like axis labels)
- TEXT/others: 0.6 (tolerate boundary errors)
Args:
region: Raw OCR text region
pp_structure_elements: List of PP-StructureV3 elements
skip_table_coverage: If True, don't consider TABLE elements as covering
(allows raw OCR text inside tables to pass through
for layered rendering)
skip_table_coverage: If True, don't consider TABLE elements as covering.
Default is False - TABLE elements DO cover regions
to prevent duplicate rendering of table cell content.
Returns:
True if the region is covered
@@ -231,10 +339,13 @@ class GapFillingService:
center_x, center_y = region.center
region_bbox = region.normalized_bbox
# Apply boundary shrinking to reduce edge duplicates
if self.shrink_pixels > 0:
region_bbox = self._shrink_bbox(region_bbox, self.shrink_pixels)
for element in pp_structure_elements:
# Skip TABLE elements when checking coverage
# This allows raw OCR text inside tables to be preserved
# PDF generator will render: table borders + raw text positions
# Check TABLE elements for coverage (default behavior)
# This prevents gap_fill from adding duplicate text inside table areas
if skip_table_coverage and element.type == ElementType.TABLE:
continue
@@ -247,9 +358,11 @@ class GapFillingService:
if self._point_in_bbox(center_x, center_y, elem_bbox):
return True
# Check 2: IoU exceeds threshold
iou = self._calculate_iou(region_bbox, elem_bbox)
if iou > self.iou_threshold:
# Check 2: IoA exceeds element-type-specific threshold
# IoA = intersection_area / ocr_box_area
ioa = self._calculate_ioa(region_bbox, elem_bbox)
threshold = self._get_ioa_threshold_for_element(element.type)
if ioa > threshold:
return True
return False
@@ -262,6 +375,9 @@ class GapFillingService:
"""
Remove regions that highly overlap with existing PP-StructureV3 TEXT elements.
Uses IoA (Intersection over Area) for deduplication to correctly detect
when an OCR region is already covered by an existing TEXT element.
Args:
uncovered_regions: List of uncovered raw OCR regions
pp_structure_elements: List of PP-StructureV3 elements
@@ -278,6 +394,11 @@ class GapFillingService:
deduplicated = []
for region in uncovered_regions:
region_bbox = region.normalized_bbox
# Apply boundary shrinking for deduplication as well
if self.shrink_pixels > 0:
region_bbox = self._shrink_bbox(region_bbox, self.shrink_pixels)
is_duplicate = False
for element in text_elements:
@@ -286,10 +407,11 @@ class GapFillingService:
element.bbox.x1, element.bbox.y1
)
iou = self._calculate_iou(region_bbox, elem_bbox)
if iou > self.dedup_iou_threshold:
# Use IoA for deduplication
ioa = self._calculate_ioa(region_bbox, elem_bbox)
if ioa > self.dedup_ioa_threshold:
logger.debug(
f"Skipping duplicate region (IoU={iou:.2f}): '{region.text[:30]}...'"
f"Skipping duplicate region (IoA={ioa:.2f}): '{region.text[:30]}...'"
)
is_duplicate = True
break
@@ -622,6 +744,52 @@ class GapFillingService:
x0, y0, x1, y1 = bbox
return x0 <= x <= x1 and y0 <= y <= y1
@staticmethod
def _calculate_ioa(
ocr_bbox: Tuple[float, float, float, float],
layout_bbox: Tuple[float, float, float, float]
) -> float:
"""
Calculate Intersection over Area (IoA) of OCR bbox relative to layout bbox.
IoA = intersection_area / ocr_box_area
This is the recommended algorithm for detecting if an OCR text region
is contained within a larger layout region. Unlike IoU which is symmetric,
IoA correctly measures "how much of the OCR box is inside the layout region".
Example:
- OCR box: 100x20 pixels (small text line)
- Layout box: 500x800 pixels (large paragraph region)
- IoU would be very small (~0.005) even if OCR is fully inside layout
- IoA would be 1.0 if OCR is fully inside layout, which is correct
Args:
ocr_bbox: OCR text region bbox (x0, y0, x1, y1) - typically smaller
layout_bbox: Layout element bbox (x0, y0, x1, y1) - typically larger
Returns:
IoA value between 0 and 1
"""
# Calculate intersection
x0 = max(ocr_bbox[0], layout_bbox[0])
y0 = max(ocr_bbox[1], layout_bbox[1])
x1 = min(ocr_bbox[2], layout_bbox[2])
y1 = min(ocr_bbox[3], layout_bbox[3])
if x1 <= x0 or y1 <= y0:
return 0.0
intersection = (x1 - x0) * (y1 - y0)
# Calculate OCR box area (denominator for IoA)
ocr_area = (ocr_bbox[2] - ocr_bbox[0]) * (ocr_bbox[3] - ocr_bbox[1])
if ocr_area <= 0:
return 0.0
return intersection / ocr_area
@staticmethod
def _calculate_iou(
bbox1: Tuple[float, float, float, float],
@@ -630,6 +798,9 @@ class GapFillingService:
"""
Calculate Intersection over Union (IoU) of two bboxes.
Note: This method is kept for backward compatibility.
For coverage detection, use _calculate_ioa() instead.
Args:
bbox1: First bbox (x0, y0, x1, y1)
bbox2: Second bbox (x0, y0, x1, y1)

101
backend/app/services/ocr_service.py
View File

@@ -6,7 +6,7 @@ Supports both PaddleOCR (for scanned documents) and direct extraction (for edita
import json
import logging
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
from typing import Any, Dict, List, Optional, Tuple, Union
from datetime import datetime
import uuid
import gc # For garbage collection
@@ -446,6 +446,47 @@ class OCRService:
except Exception as e:
logger.warning(f"Failed to clear GPU cache: {e}")
def _apply_ocr_config(self, ocr_config: 'OCRConfig'):
"""
Apply OCR configuration from preset or custom settings.
This modifies the runtime settings used by PP-Structure.
Args:
ocr_config: OCRConfig object with processing settings
"""
logger.info(f"Applying OCR config: {ocr_config.model_dump()}")
# Store the config for use in PP-Structure initialization
self._runtime_ocr_config = ocr_config
# Apply table parsing mode settings
# These will be used when initializing PP-StructureV3
settings.table_parsing_mode = ocr_config.table_parsing_mode.value if hasattr(ocr_config.table_parsing_mode, 'value') else ocr_config.table_parsing_mode
# Apply preprocessing settings
settings.use_doc_orientation_classify = ocr_config.use_doc_orientation_classify
settings.use_doc_unwarping = ocr_config.use_doc_unwarping
settings.use_textline_orientation = ocr_config.use_textline_orientation
# Apply recognition module settings
settings.enable_chart_recognition = ocr_config.enable_chart_recognition
settings.enable_formula_recognition = ocr_config.enable_formula_recognition
settings.enable_seal_recognition = ocr_config.enable_seal_recognition
settings.enable_region_detection = ocr_config.enable_region_detection
# Apply layout threshold if specified
if ocr_config.layout_threshold is not None:
settings.layout_detection_threshold = ocr_config.layout_threshold
if ocr_config.layout_nms_threshold is not None:
settings.layout_nms_threshold = ocr_config.layout_nms_threshold
# Invalidate existing structure engine to force re-initialization with new settings
if self.structure_engine is not None:
logger.info("Invalidating PP-StructureV3 engine to apply new OCR config")
self._unload_structure_engine()
logger.info(f"OCR config applied: table_parsing_mode={settings.table_parsing_mode}")
def get_ocr_engine(self, lang: str = 'ch') -> PaddleOCR:
"""
Get or create OCR engine for specified language with GPU support
@@ -615,6 +656,39 @@ class OCRService:
formula_model = settings.formula_recognition_model_name
chart_model = settings.chart_recognition_model_name
# Apply table_parsing_mode settings
# This is the KEY configuration to prevent "cell explosion" on datasheet-type documents
table_parsing_mode = settings.table_parsing_mode
logger.info(f"Table parsing mode: {table_parsing_mode}")
if table_parsing_mode == "disabled":
# 方案A: 完全關閉 table recognition
use_table = False
wired_table_model = None
wireless_table_model = None
wired_cell_det_model = None
wireless_cell_det_model = None
logger.info("Table parsing DISABLED - no cell segmentation")
elif table_parsing_mode == "classification_only":
# 方案C: 只做 table classification，不做 cell segmentation
use_table = False # Don't parse table structure
wired_table_model = None
wireless_table_model = None
wired_cell_det_model = None
wireless_cell_det_model = None
# Keep table_cls_model to identify table regions
logger.info("Table parsing CLASSIFICATION_ONLY - regions identified but no cell parsing")
elif table_parsing_mode == "conservative":
# 方案B: 保守模式 - 只禁用 wireless tables (aggressive)
# 注意：不要修改 layout_threshold，它會影響所有元素偵測，不只是表格
wireless_table_model = None
wireless_cell_det_model = None
logger.info(f"Table parsing CONSERVATIVE - wireless disabled (layout_threshold unchanged)")
# else: "full" mode - use all default settings (aggressive)
# Apply table detection config overrides for individual table types
if table_detection_config:
if not table_detection_config.enable_wired_table:
@@ -1343,6 +1417,7 @@ class OCRService:
if detect_layout:
# Pass current_page to analyze_layout for correct page numbering
# Also pass text_regions for table content rebuilding
layout_data, images_metadata = self.analyze_layout(
image_path,
output_dir=output_dir,
@@ -1350,7 +1425,8 @@ class OCRService:
layout_model=layout_model,
preprocessing_mode=preprocessing_mode,
preprocessing_config=preprocessing_config,
table_detection_config=table_detection_config
table_detection_config=table_detection_config,
raw_ocr_regions=text_regions # For table content rebuilding
)
# Generate Markdown
@@ -1379,6 +1455,12 @@ class OCRService:
# If layout data is enhanced, add enhanced results for converter
if layout_data and layout_data.get('enhanced'):
# Debug: check if table elements have rebuild_stats
for elem in layout_data.get('elements', []):
if elem.get('type') == 'table':
has_rebuild = 'rebuild_stats' in elem
logger.info(f"[OCR_SERVICE] Table {elem.get('element_id')}: has rebuild_stats={has_rebuild}, keys={list(elem.keys())[:10]}")
result['enhanced_results'] = [{
'elements': layout_data.get('elements', []),
'reading_order': layout_data.get('reading_order', []),
@@ -1509,7 +1591,8 @@ class OCRService:
layout_model: Optional[str] = None,
preprocessing_mode: Optional[PreprocessingModeEnum] = None,
preprocessing_config: Optional[PreprocessingConfig] = None,
table_detection_config: Optional[TableDetectionConfig] = None
table_detection_config: Optional[TableDetectionConfig] = None,
raw_ocr_regions: Optional[List[Dict[str, Any]]] = None
) -> Tuple[Optional[Dict], List[Dict]]:
"""
Analyze document layout using PP-StructureV3 with enhanced element extraction
@@ -1522,6 +1605,7 @@ class OCRService:
preprocessing_mode: Preprocessing mode ('auto', 'manual', 'disabled')
preprocessing_config: Manual preprocessing config (used when mode='manual')
table_detection_config: Table detection config (wired/wireless/region options)
raw_ocr_regions: Optional list of raw OCR text regions for table content rebuilding
Returns:
Tuple of (layout_data, images_metadata)
@@ -1607,7 +1691,8 @@ class OCRService:
preprocessed_image=preprocessed_image,
scaling_info=scaling_info,
save_visualization=True, # Save layout detection visualization images
use_cv_table_detection=use_cv_table_detection
use_cv_table_detection=use_cv_table_detection,
raw_ocr_regions=raw_ocr_regions # For table content rebuilding
)
if result.get('has_parsing_res_list'):
@@ -2225,7 +2310,8 @@ class OCRService:
layout_model: Optional[str] = None,
preprocessing_mode: Optional[PreprocessingModeEnum] = None,
preprocessing_config: Optional[PreprocessingConfig] = None,
table_detection_config: Optional[TableDetectionConfig] = None
table_detection_config: Optional[TableDetectionConfig] = None,
ocr_config: Optional['OCRConfig'] = None
) -> Union[UnifiedDocument, Dict]:
"""
Main processing method with dual-track support.
@@ -2242,11 +2328,16 @@ class OCRService:
preprocessing_mode: Layout preprocessing mode ('auto', 'manual', 'disabled')
preprocessing_config: Manual preprocessing config (used when mode='manual')
table_detection_config: Table detection config (wired/wireless/region options)
ocr_config: OCR processing config from preset or custom settings
Returns:
UnifiedDocument if dual-track is enabled and use_dual_track=True,
Dict with legacy format otherwise
"""
# Apply OCR config to settings if provided
if ocr_config:
self._apply_ocr_config(ocr_config)
# Use dual-track processing if:
# 1. use_dual_track is True (auto-detection), OR
# 2. force_track is specified (explicit track selection)

128
backend/app/services/ocr_to_unified_converter.py
View File

@@ -189,7 +189,7 @@ def validate_cell_boxes(
Validate cell_boxes coordinates against page boundaries and table bbox.
PP-StructureV3 sometimes returns cell_boxes with coordinates that exceed
page boundaries. This function validates and reports issues.
page boundaries or table bbox. This function validates and clamps to valid boundaries.
Args:
cell_boxes: List of cell bounding boxes [[x0, y0, x1, y1], ...]
@@ -213,10 +213,22 @@ def validate_cell_boxes(
clamped_boxes = []
# Page boundaries with tolerance
min_x = -tolerance
min_y = -tolerance
max_x = page_width + tolerance
max_y = page_height + tolerance
page_min_x = -tolerance
page_min_y = -tolerance
page_max_x = page_width + tolerance
page_max_y = page_height + tolerance
# Table boundaries with tolerance (prefer clamping to table bbox)
table_min_x = table_bbox[0] - tolerance if len(table_bbox) >= 4 else page_min_x
table_min_y = table_bbox[1] - tolerance if len(table_bbox) >= 4 else page_min_y
table_max_x = table_bbox[2] + tolerance if len(table_bbox) >= 4 else page_max_x
table_max_y = table_bbox[3] + tolerance if len(table_bbox) >= 4 else page_max_y
# For clamping, use the intersection of page and expanded table bbox
clamp_min_x = max(0, table_bbox[0] - tolerance) if len(table_bbox) >= 4 else 0
clamp_min_y = max(0, table_bbox[1] - tolerance) if len(table_bbox) >= 4 else 0
clamp_max_x = min(page_width, table_bbox[2] + tolerance) if len(table_bbox) >= 4 else page_width
clamp_max_y = min(page_height, table_bbox[3] + tolerance) if len(table_bbox) >= 4 else page_height
for idx, box in enumerate(cell_boxes):
if not box or len(box) < 4:
@@ -230,19 +242,38 @@ def validate_cell_boxes(
cell_issues = []
# Check if coordinates exceed page boundaries
if x0 < min_x:
if x0 < page_min_x:
cell_issues.append(f"x0={x0:.1f} < 0")
is_valid = False
if y0 < min_y:
if y0 < page_min_y:
cell_issues.append(f"y0={y0:.1f} < 0")
is_valid = False
if x1 > max_x:
if x1 > page_max_x:
cell_issues.append(f"x1={x1:.1f} > page_width={page_width:.1f}")
is_valid = False
if y1 > max_y:
if y1 > page_max_y:
cell_issues.append(f"y1={y1:.1f} > page_height={page_height:.1f}")
is_valid = False
# Check if coordinates significantly exceed table bbox (more than 20% of table size)
if len(table_bbox) >= 4:
table_w = table_bbox[2] - table_bbox[0]
table_h = table_bbox[3] - table_bbox[1]
expand_tolerance = max(tolerance, table_h * 0.2) # 20% of table height
if y0 < table_bbox[1] - expand_tolerance:
cell_issues.append(f"y0={y0:.1f} above table (table_y0={table_bbox[1]:.1f})")
is_valid = False
if y1 > table_bbox[3] + expand_tolerance:
cell_issues.append(f"y1={y1:.1f} below table (table_y1={table_bbox[3]:.1f})")
is_valid = False
if x0 < table_bbox[0] - expand_tolerance:
cell_issues.append(f"x0={x0:.1f} left of table (table_x0={table_bbox[0]:.1f})")
is_valid = False
if x1 > table_bbox[2] + expand_tolerance:
cell_issues.append(f"x1={x1:.1f} right of table (table_x1={table_bbox[2]:.1f})")
is_valid = False
# Check for inverted coordinates
if x0 > x1:
cell_issues.append(f"x0={x0:.1f} > x1={x1:.1f}")
@@ -255,12 +286,12 @@ def validate_cell_boxes(
invalid_count += 1
issues.append(f"Cell {idx}: {', '.join(cell_issues)}")
# Clamp to valid boundaries
# Clamp to valid boundaries (table bbox with some tolerance)
clamped_box = [
max(0, min(x0, page_width)),
max(0, min(y0, page_height)),
max(0, min(x1, page_width)),
max(0, min(y1, page_height))
max(clamp_min_x, min(x0, clamp_max_x)),
max(clamp_min_y, min(y0, clamp_max_y)),
max(clamp_min_x, min(x1, clamp_max_x)),
max(clamp_min_y, min(y1, clamp_max_y))
]
# Ensure proper ordering after clamping
@@ -395,10 +426,15 @@ class OCRToUnifiedConverter:
Handles both enhanced PP-StructureV3 results (with parsing_res_list)
and traditional markdown results. Applies gap filling when enabled.
Gap filling can use either:
1. overall_ocr_res from PP-StructureV3 (preferred, no extra inference)
2. Separate raw OCR text_regions (fallback)
"""
pages = []
# Extract raw OCR text regions for gap filling
# Prefer overall_ocr_res from PP-StructureV3 when available
raw_text_regions = ocr_results.get('text_regions', [])
ocr_dimensions = ocr_results.get('ocr_dimensions', {})
@@ -461,13 +497,22 @@ class OCRToUnifiedConverter:
if element:
elements.append(element)
# Apply gap filling if enabled and raw regions available
if self.gap_filling_service and raw_text_regions:
# Filter raw regions for current page
page_raw_regions = [
r for r in raw_text_regions
if r.get('page', 0) == page_idx or r.get('page', 1) == page_idx + 1
]
# Apply gap filling if enabled
# Priority: 1) overall_ocr_res from page_result, 2) raw_text_regions from separate OCR
if self.gap_filling_service:
# Check for overall_ocr_res from PP-StructureV3 (preferred, no extra inference)
page_raw_regions = page_result.get('overall_ocr_res', [])
if page_raw_regions:
logger.debug(f"Page {page_idx + 1}: Using overall_ocr_res ({len(page_raw_regions)} regions)")
elif raw_text_regions:
# Fallback to separate raw OCR regions
page_raw_regions = [
r for r in raw_text_regions
if r.get('page', 0) == page_idx or r.get('page', 1) == page_idx + 1
]
if page_raw_regions:
logger.debug(f"Page {page_idx + 1}: Using separate raw OCR ({len(page_raw_regions)} regions)")
if page_raw_regions:
supplemented, stats = self.gap_filling_service.fill_gaps(
@@ -711,8 +756,33 @@ class OCRToUnifiedConverter:
# Prepare content based on element type
if element_type == ElementType.TABLE:
# For tables, use TableData as content
# Pass cell_boxes for accurate cell positioning
table_data = self._extract_table_data(elem_data)
# Priority: rebuilt_table > HTML parsing
# rebuilt_table contains clean cells without empty padding
if 'rebuilt_table' in elem_data:
rebuilt = elem_data['rebuilt_table']
# Use rebuilt cells directly - they don't include empty cells
rebuilt_cells = rebuilt.get('cells', [])
from app.models.unified_document import TableCell
table_cells = [
TableCell(
row=c.get('row', 0),
col=c.get('col', 0),
row_span=c.get('row_span', 1),
col_span=c.get('col_span', 1),
content=c.get('content', '')
)
for c in rebuilt_cells
]
table_data = TableData(
rows=rebuilt.get('rows', 0),
cols=rebuilt.get('cols', 0),
cells=table_cells,
caption=elem_data.get('extracted_text')
)
logger.info(f"[CONVERTER] Table {elem_data.get('element_id')}: Using rebuilt_table directly ({len(rebuilt_cells)} cells)")
else:
# Fallback to HTML parsing for non-rebuilt tables
table_data = self._extract_table_data(elem_data)
content = table_data if table_data else elem_data.get('content', '')
# Preserve cell_boxes and embedded_images in metadata for PDF generation
@@ -756,6 +826,18 @@ class OCRToUnifiedConverter:
if 'embedded_images' in elem_data:
elem_data.setdefault('metadata', {})['embedded_images'] = elem_data['embedded_images']
# Pass through rebuild information for tables that were rebuilt
# This tells the PDF renderer to use HTML content instead of cell_boxes
logger.info(f"[CONVERTER] Table {elem_data.get('element_id')}: checking for rebuild_stats, keys={list(elem_data.keys())}")
if 'rebuild_stats' in elem_data:
elem_data.setdefault('metadata', {})['rebuild_stats'] = elem_data['rebuild_stats']
elem_data['metadata']['was_rebuilt'] = True
logger.info(f"[CONVERTER] Table {elem_data.get('element_id')}: FOUND rebuild_stats, setting was_rebuilt=True")
if 'rebuilt_table' in elem_data:
elem_data.setdefault('metadata', {})['rebuilt_table'] = elem_data['rebuilt_table']
elif element_type in [
ElementType.IMAGE, ElementType.FIGURE, ElementType.CHART,
ElementType.DIAGRAM, ElementType.LOGO, ElementType.STAMP

787
backend/app/services/pdf_generator_service.py
View File

@@ -26,6 +26,23 @@ from html.parser import HTMLParser
from app.core.config import settings
# Import table column corrector for column alignment fix
try:
from app.services.table_column_corrector import TableColumnCorrector
TABLE_COLUMN_CORRECTOR_AVAILABLE = True
except ImportError:
TABLE_COLUMN_CORRECTOR_AVAILABLE = False
TableColumnCorrector = None
# Import text region renderer for simple text positioning
try:
from app.services.text_region_renderer import TextRegionRenderer, load_raw_ocr_regions
TEXT_REGION_RENDERER_AVAILABLE = True
except ImportError:
TEXT_REGION_RENDERER_AVAILABLE = False
TextRegionRenderer = None
load_raw_ocr_regions = None
# Import UnifiedDocument for dual-track support
try:
from app.models.unified_document import (
@@ -596,7 +613,8 @@ class PDFGeneratorService:
'content': html_content,
'bbox': [element.bbox.x0, element.bbox.y0,
element.bbox.x1, element.bbox.y1],
'page': page_num - 1 # layout uses 0-based
'page': page_num - 1, # layout uses 0-based
'element_id': element.element_id # For _use_border_only matching
}
# Preserve cell_boxes and embedded_images from metadata
@@ -607,18 +625,29 @@ class PDFGeneratorService:
table_element['cell_boxes_source'] = element.metadata.get('cell_boxes_source', 'metadata')
if 'embedded_images' in element.metadata:
table_element['embedded_images'] = element.metadata['embedded_images']
# Pass through rebuild flag - rebuilt tables should use HTML content
if element.metadata.get('was_rebuilt'):
table_element['was_rebuilt'] = True
logger.debug(f"Table {element.element_id}: marked as rebuilt")
layout_elements.append(table_element)
# Add bbox to images_metadata for text overlap filtering
# (no actual image file, just bbox for filtering)
images_metadata.append({
img_metadata = {
'image_path': None, # No fake table image
'bbox': bbox_polygon,
'page': page_num - 1, # 0-based for images_metadata
'type': 'table',
'element_id': element.element_id
})
}
# Also copy cell_boxes for quality checking
if element.metadata and 'cell_boxes' in element.metadata:
img_metadata['cell_boxes'] = element.metadata['cell_boxes']
# Mark if table was rebuilt
if element.metadata and element.metadata.get('was_rebuilt'):
img_metadata['was_rebuilt'] = True
images_metadata.append(img_metadata)
# Handle image/visual elements (including stamps/seals)
elif element.is_visual or element.type in [
@@ -1022,15 +1051,25 @@ class PDFGeneratorService:
# Set current track
self.current_processing_track = 'ocr'
# Convert UnifiedDocument to OCR data format (legacy)
ocr_data = self.convert_unified_document_to_ocr_data(unified_doc)
# Check if simple text positioning mode is enabled
if (settings.simple_text_positioning_enabled and
TEXT_REGION_RENDERER_AVAILABLE):
logger.info("Using simple text positioning mode")
result = self._generate_simple_text_pdf(
unified_doc=unified_doc,
output_path=output_path,
source_file_path=source_file_path
)
else:
# Convert UnifiedDocument to OCR data format (legacy)
ocr_data = self.convert_unified_document_to_ocr_data(unified_doc)
# Use existing generation pipeline
result = self._generate_pdf_from_data(
ocr_data=ocr_data,
output_path=output_path,
source_file_path=source_file_path
)
# Use existing generation pipeline
result = self._generate_pdf_from_data(
ocr_data=ocr_data,
output_path=output_path,
source_file_path=source_file_path
)
# Reset track
self.current_processing_track = None
@@ -1043,6 +1082,235 @@ class PDFGeneratorService:
self.current_processing_track = None
return False
def _generate_simple_text_pdf(
self,
unified_doc: 'UnifiedDocument',
output_path: Path,
source_file_path: Optional[Path] = None
) -> bool:
"""
Generate PDF using simple text positioning from raw OCR regions.
This approach bypasses complex table structure reconstruction and renders
raw OCR text directly at detected positions with rotation correction.
Images, charts, figures, seals, and formulas are still rendered normally.
Args:
unified_doc: UnifiedDocument from OCR processing
output_path: Path to save generated PDF
source_file_path: Optional path to original source file
Returns:
True if successful, False otherwise
"""
try:
logger.info("=== Simple Text Positioning PDF Generation ===")
# Initialize text region renderer
text_renderer = TextRegionRenderer(
font_name=self.font_name,
debug=settings.simple_text_positioning_debug
)
# Get result directory from output_path
result_dir = output_path.parent
# Try to determine task_id from result directory or output filename
# Output path is typically: result_dir/task_id_edited.pdf
task_id = None
if output_path.stem.endswith('_edited'):
task_id = output_path.stem.replace('_edited', '')
elif result_dir.name:
# result_dir is typically the task_id directory
task_id = result_dir.name
if not task_id:
logger.warning("Could not determine task_id, falling back to legacy method")
ocr_data = self.convert_unified_document_to_ocr_data(unified_doc)
return self._generate_pdf_from_data(
ocr_data=ocr_data,
output_path=output_path,
source_file_path=source_file_path
)
logger.info(f"Task ID: {task_id}, Result dir: {result_dir}")
# Get total pages from UnifiedDocument
total_pages = len(unified_doc.pages) if unified_doc.pages else 1
# Get page dimensions from first page (for canvas initialization)
if not unified_doc.pages:
logger.error("No pages in document")
return False
first_page = unified_doc.pages[0]
if hasattr(first_page, 'dimensions') and first_page.dimensions:
page_width = float(first_page.dimensions.width)
page_height = float(first_page.dimensions.height)
else:
# Fallback to default size
page_width = 612.0 # Letter width
page_height = 792.0 # Letter height
logger.warning(f"No page dimensions found, using default {page_width}x{page_height}")
logger.info(f"Initial page size: {page_width:.1f} x {page_height:.1f}")
# Create PDF canvas
pdf_canvas = canvas.Canvas(str(output_path), pagesize=(page_width, page_height))
# Collect image-type elements from UnifiedDocument for rendering
# Types that should be rendered as images: figure, image, chart, seal, formula
image_element_types = {'figure', 'image', 'chart', 'seal', 'formula'}
# Process each page
for page_num in range(1, total_pages + 1):
logger.info(f">>> Processing page {page_num}/{total_pages}")
# Get page dimensions for current page
if page_num <= len(unified_doc.pages):
current_page = unified_doc.pages[page_num - 1]
if hasattr(current_page, 'dimensions') and current_page.dimensions:
current_width = float(current_page.dimensions.width)
current_height = float(current_page.dimensions.height)
else:
current_width = page_width
current_height = page_height
else:
current_width = page_width
current_height = page_height
if page_num > 1:
pdf_canvas.showPage()
# Set page size
pdf_canvas.setPageSize((current_width, current_height))
# === Layer 1: Render images, charts, figures, seals, formulas ===
# Also collect exclusion zones for text avoidance
exclusion_zones = [] # List of (x0, y0, x1, y1) tuples
if page_num <= len(unified_doc.pages):
current_page = unified_doc.pages[page_num - 1]
page_elements = current_page.elements if hasattr(current_page, 'elements') else []
image_elements_rendered = 0
for elem in page_elements:
elem_type = elem.type if hasattr(elem, 'type') else elem.get('type', '')
# Handle enum type
if hasattr(elem_type, 'value'):
elem_type = elem_type.value
if elem_type in image_element_types:
# Get image path from element content
content = elem.content if hasattr(elem, 'content') else elem.get('content', {})
if isinstance(content, dict):
saved_path = content.get('saved_path') or content.get('path')
else:
saved_path = None
# Get bbox for exclusion zone (even if image file not found)
bbox = elem.bbox if hasattr(elem, 'bbox') else elem.get('bbox', {})
if hasattr(bbox, 'x0'):
x0, y0, x1, y1 = bbox.x0, bbox.y0, bbox.x1, bbox.y1
elif isinstance(bbox, dict):
x0 = bbox.get('x0', 0)
y0 = bbox.get('y0', 0)
x1 = bbox.get('x1', x0 + bbox.get('width', 0))
y1 = bbox.get('y1', y0 + bbox.get('height', 0))
else:
continue
# Add to exclusion zones for text avoidance
# Use original image coordinates (not PDF flipped)
exclusion_zones.append((x0, y0, x1, y1))
if saved_path:
# Try to find the image file
image_path = result_dir / saved_path
if not image_path.exists():
# Try in imgs subdirectory
image_path = result_dir / 'imgs' / saved_path
if not image_path.exists():
# Try just the filename
image_path = result_dir / Path(saved_path).name
if image_path.exists():
try:
# Convert coordinates (flip Y for PDF)
pdf_x = x0
pdf_y = current_height - y1 # Bottom of image in PDF coords
img_width = x1 - x0
img_height = y1 - y0
# Draw image
pdf_canvas.drawImage(
str(image_path),
pdf_x, pdf_y,
width=img_width,
height=img_height,
preserveAspectRatio=True,
mask='auto'
)
image_elements_rendered += 1
logger.debug(f"Rendered {elem_type}: {saved_path} at ({pdf_x:.1f}, {pdf_y:.1f})")
except Exception as e:
logger.warning(f"Failed to render {elem_type} {saved_path}: {e}")
else:
logger.warning(f"Image file not found: {saved_path}")
if image_elements_rendered > 0:
logger.info(f"Rendered {image_elements_rendered} image elements (figures/charts/seals/formulas)")
if exclusion_zones:
logger.info(f"Collected {len(exclusion_zones)} exclusion zones for text avoidance")
# === Layer 2: Render text from raw OCR regions ===
raw_regions = load_raw_ocr_regions(str(result_dir), task_id, page_num)
if not raw_regions:
logger.warning(f"No raw OCR regions found for page {page_num}")
else:
logger.info(f"Loaded {len(raw_regions)} raw OCR regions for page {page_num}")
# Collect texts inside exclusion zones for position-aware deduplication
# This prevents duplicate axis labels from being rendered near charts
zone_texts = None
if exclusion_zones:
zone_texts = text_renderer.collect_zone_texts(
raw_regions, exclusion_zones, threshold=0.5, include_axis_labels=True
)
if zone_texts:
logger.info(f"Collected {len(zone_texts)} zone texts for deduplication: {list(zone_texts)[:10]}...")
# Render all text regions, avoiding exclusion zones (images/charts)
# Scale factors are 1.0 since OCR dimensions match page dimensions
rendered = text_renderer.render_all_regions(
pdf_canvas=pdf_canvas,
regions=raw_regions,
page_height=current_height,
scale_x=1.0,
scale_y=1.0,
exclusion_zones=exclusion_zones,
zone_texts=zone_texts
)
logger.info(f"Rendered {rendered} text regions")
logger.info(f"<<< Page {page_num} complete")
# Save PDF
pdf_canvas.save()
file_size = output_path.stat().st_size
logger.info(f"Generated PDF: {output_path.name} ({file_size} bytes)")
return True
except Exception as e:
logger.error(f"Failed to generate simple text PDF: {e}")
import traceback
traceback.print_exc()
return False
def _generate_pdf_from_data(
self,
ocr_data: Dict,
@@ -1093,8 +1361,15 @@ class PDFGeneratorService:
logger.info("No page_dimensions found, using first page size for all pages")
# Step 3: Get original file dimensions for all pages
# For OCR track, we use OCR coordinate system dimensions directly to avoid scaling issues
original_page_sizes = {}
if source_file_path:
use_ocr_dimensions_for_pdf = (self.current_processing_track == 'ocr')
if use_ocr_dimensions_for_pdf:
# OCR Track: Use OCR coordinate system dimensions directly
# This ensures no scaling is needed (scale = 1.0)
logger.info(f"OCR Track: 使用 OCR 座標系尺寸作為 PDF 頁面尺寸（避免縮放）")
elif source_file_path:
original_page_sizes = self.get_all_page_sizes(source_file_path)
if original_page_sizes:
logger.info(f"從原始文件獲取到 {len(original_page_sizes)} 頁尺寸")
@@ -1104,8 +1379,12 @@ class PDFGeneratorService:
logger.info(f"無原始文件，將使用 OCR/UnifiedDocument 尺寸")
# Determine initial canvas size (will be updated per page)
# Priority: original file first page > OCR/UnifiedDocument first page
if 0 in original_page_sizes:
# Priority for OCR track: OCR dimensions (no scaling)
# Priority for Direct track: original file first page > OCR/UnifiedDocument first page
if use_ocr_dimensions_for_pdf:
target_width, target_height = ocr_width, ocr_height
logger.info(f"初始 PDF 尺寸（OCR Track, 使用 OCR 座標系）: {target_width:.1f} x {target_height:.1f}")
elif 0 in original_page_sizes:
target_width, target_height = original_page_sizes[0]
logger.info(f"初始 PDF 尺寸（來自原始文件首頁）: {target_width:.1f} x {target_height:.1f}")
else:
@@ -1159,14 +1438,49 @@ class PDFGeneratorService:
# Create PDF canvas with initial page size (will be updated per page)
pdf_canvas = canvas.Canvas(str(output_path), pagesize=(target_width, target_height))
# LAYERED RENDERING: Exclude tables from regions_to_avoid
# Text inside tables will be rendered at raw OCR positions (via GapFillingService)
# while table borders are drawn separately using cell_boxes
# Only avoid overlap with actual images/figures/charts
regions_to_avoid = [img for img in images_metadata if img.get('type') != 'table']
table_count = len([img for img in images_metadata if img.get('type') == 'table'])
# Smart filtering: only include tables with good cell_boxes quality in regions_to_avoid
# Tables with bad cell_boxes will use raw OCR text positioning instead
# Exception: Rebuilt tables always use HTML content and filter text
regions_to_avoid = []
good_quality_tables = []
bad_quality_tables = []
rebuilt_tables = []
logger.info(f"過濾文字區域: {len(regions_to_avoid)} 個區域需要避免 (不含表格), {table_count} 個表格使用分層渲染")
for img in images_metadata:
if img.get('type') == 'table':
elem_id = img.get('element_id', 'unknown')
# Check if this table was rebuilt - rebuilt tables have good content
was_rebuilt = img.get('was_rebuilt', False)
if was_rebuilt:
# Rebuilt tables have accurate content - filter text, use HTML
regions_to_avoid.append(img)
rebuilt_tables.append(elem_id)
else:
# Check cell_boxes quality for non-rebuilt tables
cell_boxes = img.get('cell_boxes', [])
quality = self._check_cell_boxes_quality(cell_boxes, elem_id)
if quality == 'good':
# Good quality: filter text, render with cell_boxes
regions_to_avoid.append(img)
good_quality_tables.append(elem_id)
else:
# Bad quality: don't filter text, just draw border
bad_quality_tables.append(elem_id)
img['_use_border_only'] = True # Mark for border-only rendering
else:
# Non-table elements (images, figures, charts) always avoid
regions_to_avoid.append(img)
logger.info(f"過濾文字區域: {len(regions_to_avoid)} 個區域需要避免")
if rebuilt_tables:
logger.info(f" 重建表格用 HTML: {rebuilt_tables}")
if good_quality_tables:
logger.info(f" 表格用 cell_boxes: {good_quality_tables}")
if bad_quality_tables:
logger.info(f" 表格用 raw OCR text (border only): {bad_quality_tables}")
filtered_text_regions = self._filter_text_in_regions(text_regions, regions_to_avoid)
@@ -1178,10 +1492,24 @@ class PDFGeneratorService:
pages_data[page_num] = []
pages_data[page_num].append(region)
# Get table elements from layout_data
# Get table elements from layout_data and copy _use_border_only flags
table_elements = []
if layout_data and layout_data.get('elements'):
table_elements = [e for e in layout_data['elements'] if e.get('type') == 'table']
# Create a lookup for _use_border_only flags from images_metadata
border_only_tables = {img.get('element_id') for img in images_metadata
if img.get('type') == 'table' and img.get('_use_border_only')}
logger.debug(f"[DEBUG] border_only_tables from images_metadata: {border_only_tables}")
for e in layout_data['elements']:
if e.get('type') == 'table':
elem_id = e.get('element_id')
logger.debug(f"[DEBUG] layout_data table element_id: {elem_id}")
# Copy the flag if this table should use border only
if elem_id in border_only_tables:
e['_use_border_only'] = True
logger.info(f"[DEBUG] Set _use_border_only=True for table {elem_id}")
table_elements.append(e)
# Process each page
total_pages = ocr_data.get('total_pages', 1)
@@ -1195,14 +1523,23 @@ class PDFGeneratorService:
logger.info(f">>> 處理第 {page_num}/{total_pages} 頁")
# Get current page dimensions with priority order:
# 1. Original file dimensions (highest priority)
# 2. OCR/UnifiedDocument dimensions
# 3. Fallback to first page dimensions
# For OCR Track: always use OCR dimensions (scale = 1.0)
# For Direct Track:
# 1. Original file dimensions (highest priority)
# 2. OCR/UnifiedDocument dimensions
# 3. Fallback to first page dimensions
page_idx = page_num - 1
dimension_source = "unknown"
# Priority 1: Original file dimensions
if page_idx in original_page_sizes:
# For OCR Track: always use OCR dimensions
if use_ocr_dimensions_for_pdf and page_idx in page_dimensions:
current_page_dims = page_dimensions[page_idx]
current_target_w = float(current_page_dims['width'])
current_target_h = float(current_page_dims['height'])
dimension_source = "ocr_track_direct"
# Priority 1: Original file dimensions (Direct Track only)
elif page_idx in original_page_sizes:
current_target_w, current_target_h = original_page_sizes[page_idx]
dimension_source = "original_file"
@@ -1774,12 +2111,26 @@ class PDFGeneratorService:
non_empty_lines = [l for l in lines if l.strip()]
num_lines = max(len(non_empty_lines), 1)
# Font size = bbox_height / num_lines * factor
# Font size calculation with stabilization
# Use 0.8 factor to leave room for line spacing
font_size = (bbox_height / num_lines) * 0.8
font_size = max(min(font_size, 72), 4) # Clamp between 4pt and 72pt
raw_font_size = (bbox_height / num_lines) * 0.8
logger.debug(f"Text has {num_lines} non-empty lines, bbox_height={bbox_height:.1f}, calculated font_size={font_size:.1f}")
# Stabilize font size for body text (most common case)
# Normal body text should be 9-11pt, only deviate for clear outliers
element_type = region.get('element_type', 'text')
if element_type in ('text', 'paragraph'):
# For body text, bias toward 10pt baseline
if 7 <= raw_font_size <= 14:
# Near-normal range: use weighted average toward 10pt
font_size = raw_font_size * 0.7 + 10 * 0.3
else:
# Clear outlier: use raw but clamp more aggressively
font_size = max(min(raw_font_size, 14), 7)
else:
# For titles/headers/etc, use raw calculation with wider range
font_size = max(min(raw_font_size, 72), 4)
logger.debug(f"Text has {num_lines} non-empty lines, bbox_height={bbox_height:.1f}, raw={raw_font_size:.1f}, final={font_size:.1f}")
# Transform coordinates: OCR (top-left origin) → PDF (bottom-left origin)
# CRITICAL: Y-axis flip!
@@ -2008,24 +2359,45 @@ class PDFGeneratorService:
result_dir: Directory containing result files (for embedded images)
"""
try:
elem_id = table_element.get('element_id', 'unknown')
use_border_only = table_element.get('_use_border_only', False)
logger.info(f"[DEBUG] draw_table_region: elem_id={elem_id}, _use_border_only={use_border_only}")
html_content = table_element.get('content', '')
if not html_content:
# Even without HTML, draw border if requested
if use_border_only:
self._draw_table_border_only(pdf_canvas, table_element, page_height, scale_w, scale_h)
return
# Try to use cell_boxes for direct rendering first (more accurate)
# Apply column correction if enabled
cell_boxes = table_element.get('cell_boxes', [])
if cell_boxes:
logger.info(f"[TABLE] Using cell_boxes direct rendering ({len(cell_boxes)} cells)")
success = self._draw_table_with_cell_boxes(
pdf_canvas, table_element, page_height,
scale_w, scale_h, result_dir
)
if success:
return # Successfully rendered with cell_boxes
if (settings.table_column_correction_enabled and
TABLE_COLUMN_CORRECTOR_AVAILABLE and
cell_boxes):
try:
corrector = TableColumnCorrector(
correction_threshold=settings.table_column_correction_threshold,
vertical_merge_enabled=settings.vertical_fragment_merge_enabled,
vertical_aspect_ratio=settings.vertical_fragment_aspect_ratio
)
# Get table bbox for vertical fragment detection
table_bbox = table_element.get('bbox', [])
if isinstance(table_bbox, dict):
table_bbox = [table_bbox['x0'], table_bbox['y0'], table_bbox['x1'], table_bbox['y1']]
logger.info("[TABLE] Falling back to ReportLab Table")
corrected_html, stats = corrector.correct(
html=html_content,
cell_boxes=cell_boxes,
table_bbox=table_bbox if isinstance(table_bbox, list) and len(table_bbox) >= 4 else None
)
if stats.get('column_corrections', 0) > 0:
logger.info(f"[TABLE] {elem_id}: Column correction applied - {stats}")
html_content = corrected_html
except Exception as e:
logger.warning(f"[TABLE] {elem_id}: Column correction failed: {e}, using original HTML")
# Fallback: Parse HTML to extract table structure and use ReportLab Table
# Parse HTML first to get table structure for grid validation
parser = HTMLTableParser()
parser.feed(html_content)
@@ -2040,6 +2412,83 @@ class PDFGeneratorService:
if not rows:
return
# Calculate number of rows and columns from HTML for grid validation
num_rows = len(rows)
max_cols = 0
for row in rows:
row_cols = sum(cell.get('colspan', 1) for cell in row['cells'])
max_cols = max(max_cols, row_cols)
# Check if table was rebuilt - if so, use HTML content directly
was_rebuilt = table_element.get('was_rebuilt', False)
cell_boxes_rendered = False # Track if we rendered borders with cell_boxes
if was_rebuilt:
logger.info(f"[TABLE] {elem_id}: Table was rebuilt, using HTML content directly")
elif use_border_only:
# Bad quality cell_boxes: skip cell_boxes rendering, use ReportLab Table with borders
logger.info(f"[TABLE] {elem_id}: Bad cell_boxes quality, using ReportLab Table with borders")
else:
# Check if cell_boxes can produce a valid grid before rendering borders
cell_boxes = table_element.get('cell_boxes', [])
if cell_boxes:
# Get table bbox for grid calculation
temp_bbox = table_element.get('bbox', [])
if isinstance(temp_bbox, dict):
raw_bbox = [temp_bbox['x0'], temp_bbox['y0'], temp_bbox['x1'], temp_bbox['y1']]
elif isinstance(temp_bbox, list) and len(temp_bbox) >= 4:
if isinstance(temp_bbox[0], (int, float)):
raw_bbox = temp_bbox[:4]
else:
raw_bbox = [temp_bbox[0][0], temp_bbox[0][1], temp_bbox[2][0], temp_bbox[2][1]]
else:
raw_bbox = None
# Pre-check: can we compute a valid grid from cell_boxes?
if raw_bbox:
test_col_widths, test_row_heights = self._compute_table_grid_from_cell_boxes(
cell_boxes, raw_bbox, num_rows, max_cols
)
grid_valid = test_col_widths is not None and test_row_heights is not None
if grid_valid:
logger.info(f"[TABLE] Grid validation passed, rendering borders with cell_boxes")
success = self._draw_table_with_cell_boxes(
pdf_canvas, table_element, page_height,
scale_w, scale_h, result_dir
)
if success:
cell_boxes_rendered = True
logger.info("[TABLE] cell_boxes rendered borders, continuing with text-only ReportLab Table")
else:
logger.info("[TABLE] cell_boxes rendering failed, using ReportLab Table with borders")
else:
# Grid mismatch: try cellboxes-first rendering if enabled
if settings.table_rendering_prefer_cellboxes:
logger.info(f"[TABLE] Grid mismatch, trying cellboxes-first rendering")
from app.services.pdf_table_renderer import TableRenderer, TableRenderConfig
renderer = TableRenderer(TableRenderConfig())
success = renderer.render_from_cellboxes_grid(
pdf_canvas,
cell_boxes,
html_content,
tuple(raw_bbox),
page_height,
scale_w,
scale_h,
row_threshold=settings.table_cellboxes_row_threshold,
col_threshold=settings.table_cellboxes_col_threshold
)
if success:
logger.info("[TABLE] cellboxes-first rendering succeeded, skipping HTML-based rendering")
return # Table fully rendered, exit early
else:
logger.info("[TABLE] cellboxes-first rendering failed, falling back to HTML-based")
else:
logger.info(f"[TABLE] Grid validation failed (mismatch), using ReportLab Table with borders")
else:
logger.info("[TABLE] No valid bbox for grid validation, using ReportLab Table with borders")
# Get bbox directly from table element
table_bbox = table_element.get('bbox')
@@ -2106,15 +2555,7 @@ class PDFGeneratorService:
pdf_y = page_height - ocr_y_bottom
# Build table data for ReportLab with proper colspan/rowspan handling
# First pass: determine the actual grid size by accounting for spans
num_rows = len(rows)
# Calculate actual number of columns by checking first row's total span
max_cols = 0
for row in rows:
row_cols = sum(cell.get('colspan', 1) for cell in row['cells'])
max_cols = max(max_cols, row_cols)
# num_rows and max_cols already calculated above for grid validation
logger.info(f"[表格] {num_rows}行x{max_cols}列 → PDF位置: ({pdf_x:.1f}, {pdf_y:.1f}), 寬x高: {table_width:.0f}x{table_height:.0f}")
# Create a grid to track occupied cells (for rowspan handling)
@@ -2223,16 +2664,25 @@ class PDFGeneratorService:
logger.info(f"[TABLE] Created with {len(col_widths)} cols, {len(row_heights)} rows")
# Apply table style
style = TableStyle([
# If cell_boxes rendered borders, skip GRID style (text-only rendering)
style_commands = [
('FONT', (0, 0), (-1, -1), self.font_name if self.font_registered else 'Helvetica', font_size),
('GRID', (0, 0), (-1, -1), 0.5, colors.black),
('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('LEFTPADDING', (0, 0), (-1, -1), 2),
('RIGHTPADDING', (0, 0), (-1, -1), 2),
('TOPPADDING', (0, 0), (-1, -1), 2),
('BOTTOMPADDING', (0, 0), (-1, -1), 2),
])
]
# Only add GRID if cell_boxes didn't render borders
if not cell_boxes_rendered:
style_commands.insert(1, ('GRID', (0, 0), (-1, -1), 0.5, colors.black))
logger.info("[TABLE] Adding GRID style (cell_boxes not used)")
else:
logger.info("[TABLE] Skipping GRID style (cell_boxes rendered borders)")
style = TableStyle(style_commands)
# Add header style if first row has headers
if rows and rows[0]['cells'] and rows[0]['cells'][0].get('is_header'):
@@ -2435,6 +2885,106 @@ class PDFGeneratorService:
logger.debug(f"[TABLE] Normalized {len(cell_boxes)} cell boxes to grid")
return normalized_boxes
def _draw_table_border_only(
self,
pdf_canvas: canvas.Canvas,
table_element: Dict,
page_height: float,
scale_w: float = 1.0,
scale_h: float = 1.0
):
"""
Draw only the outer border of a table (for tables with bad cell_boxes quality).
Text inside the table will be rendered using raw OCR positions.
Args:
pdf_canvas: ReportLab canvas object
table_element: Table element dict
page_height: Height of page in PDF coordinates
scale_w: Scale factor for X coordinates
scale_h: Scale factor for Y coordinates
"""
table_bbox = table_element.get('bbox', [])
if not table_bbox or len(table_bbox) < 4:
return
element_id = table_element.get('element_id', 'unknown')
# Handle different bbox formats
if isinstance(table_bbox, dict):
x0, y0, x1, y1 = table_bbox['x0'], table_bbox['y0'], table_bbox['x1'], table_bbox['y1']
elif isinstance(table_bbox[0], (int, float)):
x0, y0, x1, y1 = table_bbox[0], table_bbox[1], table_bbox[2], table_bbox[3]
else:
return
# Apply scaling
pdf_x0 = x0 * scale_w
pdf_y0 = y0 * scale_h
pdf_x1 = x1 * scale_w
pdf_y1 = y1 * scale_h
# Convert to PDF coordinates (flip Y)
pdf_top = page_height - pdf_y0
pdf_bottom = page_height - pdf_y1
width = pdf_x1 - pdf_x0
height = pdf_y1 - pdf_y0
# Draw outer border only
pdf_canvas.setStrokeColor(colors.black)
pdf_canvas.setLineWidth(0.5)
pdf_canvas.rect(pdf_x0, pdf_bottom, width, height, stroke=1, fill=0)
logger.info(f"[TABLE] {element_id}: Drew border only (bad cell_boxes quality)")
def _check_cell_boxes_quality(self, cell_boxes: List, element_id: str = "") -> str:
"""
Check the quality of cell_boxes to determine rendering strategy.
Args:
cell_boxes: List of cell bounding boxes
element_id: Optional element ID for logging
Returns:
'good' if cell_boxes form a proper grid, 'bad' otherwise
"""
# If quality check is disabled, always return 'good' to use pure PP-Structure output
if not settings.table_quality_check_enabled:
logger.debug(f"[TABLE QUALITY] {element_id}: good - quality check disabled (pure PP-Structure mode)")
return 'good'
if not cell_boxes or len(cell_boxes) < 2:
logger.debug(f"[TABLE QUALITY] {element_id}: bad - too few cells ({len(cell_boxes) if cell_boxes else 0})")
return 'bad' # No cell_boxes or too few
# Count overlapping cell pairs
overlap_count = 0
for i, box1 in enumerate(cell_boxes):
for j, box2 in enumerate(cell_boxes):
if i >= j:
continue
if not isinstance(box1, (list, tuple)) or len(box1) < 4:
continue
if not isinstance(box2, (list, tuple)) or len(box2) < 4:
continue
x_overlap = box1[0] < box2[2] and box1[2] > box2[0]
y_overlap = box1[1] < box2[3] and box1[3] > box2[1]
if x_overlap and y_overlap:
overlap_count += 1
total_pairs = len(cell_boxes) * (len(cell_boxes) - 1) // 2
overlap_ratio = overlap_count / total_pairs if total_pairs > 0 else 0
# Relaxed threshold: 20% overlap instead of 10% to allow more tables through
# This is because PP-StructureV3's cell detection sometimes has slight overlaps
if overlap_ratio > 0.20:
logger.info(f"[TABLE QUALITY] {element_id}: bad - overlap ratio {overlap_ratio:.2%} > 20%")
return 'bad'
logger.debug(f"[TABLE QUALITY] {element_id}: good - {len(cell_boxes)} cells, overlap {overlap_ratio:.2%}")
return 'good'
def _draw_table_with_cell_boxes(
self,
pdf_canvas: canvas.Canvas,
@@ -2465,39 +3015,64 @@ class PDFGeneratorService:
"""
try:
cell_boxes = table_element.get('cell_boxes', [])
# Always draw outer table border first (fallback for incomplete cell_boxes)
table_bbox = table_element.get('bbox', [])
if table_bbox and len(table_bbox) >= 4:
# Handle different bbox formats (list or dict)
if isinstance(table_bbox, dict):
tx1 = float(table_bbox.get('x0', 0))
ty1 = float(table_bbox.get('y0', 0))
tx2 = float(table_bbox.get('x1', 0))
ty2 = float(table_bbox.get('y1', 0))
else:
tx1, ty1, tx2, ty2 = table_bbox[:4]
# Apply scaling
tx1_scaled = tx1 * scale_w
ty1_scaled = ty1 * scale_h
tx2_scaled = tx2 * scale_w
ty2_scaled = ty2 * scale_h
# Check cell_boxes quality - skip if they don't form a proper grid
if cell_boxes and len(cell_boxes) > 2:
# Count overlapping cell pairs
overlap_count = 0
for i, box1 in enumerate(cell_boxes):
for j, box2 in enumerate(cell_boxes):
if i >= j:
continue
x_overlap = box1[0] < box2[2] and box1[2] > box2[0]
y_overlap = box1[1] < box2[3] and box1[3] > box2[1]
if x_overlap and y_overlap:
overlap_count += 1
table_width = tx2_scaled - tx1_scaled
table_height = ty2_scaled - ty1_scaled
# If more than 25% of cell pairs overlap, cell_boxes are unreliable
# Increased from 10% to 25% to allow more tables to use cell_boxes rendering
# which provides better visual fidelity than ReportLab Table fallback
total_pairs = len(cell_boxes) * (len(cell_boxes) - 1) // 2
overlap_ratio = overlap_count / total_pairs if total_pairs > 0 else 0
# Transform Y coordinate (PDF uses bottom-left origin)
pdf_x = tx1_scaled
pdf_y = page_height - ty2_scaled # Bottom of table in PDF coords
# Draw outer table border (slightly thicker for visibility)
pdf_canvas.setStrokeColor(colors.black)
pdf_canvas.setLineWidth(1.0)
pdf_canvas.rect(pdf_x, pdf_y, table_width, table_height, stroke=1, fill=0)
logger.info(f"[TABLE] Drew outer table border at [{int(tx1)},{int(ty1)},{int(tx2)},{int(ty2)}]")
if overlap_ratio > 0.25:
logger.warning(
f"[TABLE] Skipping cell_boxes rendering: {overlap_count}/{total_pairs} "
f"({overlap_ratio:.1%}) cell pairs overlap - using ReportLab Table fallback"
)
return False # Return False to trigger ReportLab Table fallback
if not cell_boxes:
# Fallback: draw outer border only when no cell_boxes
if table_bbox and len(table_bbox) >= 4:
# Handle different bbox formats (list or dict)
if isinstance(table_bbox, dict):
tx1 = float(table_bbox.get('x0', 0))
ty1 = float(table_bbox.get('y0', 0))
tx2 = float(table_bbox.get('x1', 0))
ty2 = float(table_bbox.get('y1', 0))
else:
tx1, ty1, tx2, ty2 = table_bbox[:4]
# Apply scaling
tx1_scaled = tx1 * scale_w
ty1_scaled = ty1 * scale_h
tx2_scaled = tx2 * scale_w
ty2_scaled = ty2 * scale_h
table_width = tx2_scaled - tx1_scaled
table_height = ty2_scaled - ty1_scaled
# Transform Y coordinate (PDF uses bottom-left origin)
pdf_x = tx1_scaled
pdf_y = page_height - ty2_scaled # Bottom of table in PDF coords
# Draw outer table border (slightly thicker for visibility)
pdf_canvas.setStrokeColor(colors.black)
pdf_canvas.setLineWidth(1.0)
pdf_canvas.rect(pdf_x, pdf_y, table_width, table_height, stroke=1, fill=0)
logger.info(f"[TABLE] Drew outer table border at [{int(tx1)},{int(ty1)},{int(tx2)},{int(ty2)}]")
logger.warning("[TABLE] No cell_boxes available, only outer border drawn")
# Still draw embedded images even without cell borders
embedded_images = table_element.get('embedded_images', [])
@@ -2511,31 +3086,47 @@ class PDFGeneratorService:
# Normalize cell boxes to create aligned grid
cell_boxes = self._normalize_cell_boxes_to_grid(cell_boxes)
logger.info(f"[TABLE] Drawing {len(cell_boxes)} cell borders (layered mode, grid-aligned)")
logger.info(f"[TABLE] Drawing {len(cell_boxes)} cells using grid lines (avoiding duplicates)")
# Collect unique grid lines to avoid drawing duplicate/overlapping lines
h_lines = set() # Horizontal lines: (y, x_start, x_end)
v_lines = set() # Vertical lines: (x, y_start, y_end)
# Draw each cell border
for box in cell_boxes:
x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
# Apply scaling
x1_scaled = x1 * scale_w
y1_scaled = y1 * scale_h
x2_scaled = x2 * scale_w
y2_scaled = y2 * scale_h
x1_s = x1 * scale_w
y1_s = y1 * scale_h
x2_s = x2 * scale_w
y2_s = y2 * scale_h
cell_width = x2_scaled - x1_scaled
cell_height = y2_scaled - y1_scaled
# Round to 1 decimal place to help with deduplication
x1_s, y1_s, x2_s, y2_s = round(x1_s, 1), round(y1_s, 1), round(x2_s, 1), round(y2_s, 1)
# Transform Y coordinate (PDF uses bottom-left origin)
pdf_x = x1_scaled
pdf_y = page_height - y2_scaled # Bottom of cell in PDF coords
# Add horizontal lines (top and bottom of cell)
h_lines.add((y1_s, x1_s, x2_s)) # Top line
h_lines.add((y2_s, x1_s, x2_s)) # Bottom line
# Draw cell border only (no fill, no text)
pdf_canvas.setStrokeColor(colors.black)
pdf_canvas.setLineWidth(0.5)
pdf_canvas.rect(pdf_x, pdf_y, cell_width, cell_height, stroke=1, fill=0)
# Add vertical lines (left and right of cell)
v_lines.add((x1_s, y1_s, y2_s)) # Left line
v_lines.add((x2_s, y1_s, y2_s)) # Right line
logger.info(f"[TABLE] Drew {len(cell_boxes)} cell borders")
# Draw unique horizontal lines
pdf_canvas.setStrokeColor(colors.black)
pdf_canvas.setLineWidth(0.5)
for y, x_start, x_end in h_lines:
pdf_y = page_height - y # Transform Y coordinate
pdf_canvas.line(x_start, pdf_y, x_end, pdf_y)
# Draw unique vertical lines
for x, y_start, y_end in v_lines:
pdf_y_start = page_height - y_start
pdf_y_end = page_height - y_end
pdf_canvas.line(x, pdf_y_start, x, pdf_y_end)
logger.info(f"[TABLE] Drew {len(h_lines)} horizontal + {len(v_lines)} vertical grid lines")
# Draw embedded images
embedded_images = table_element.get('embedded_images', [])

391
backend/app/services/pdf_table_renderer.py
View File

@@ -24,6 +24,256 @@ from reportlab.platypus import Paragraph, Table, TableStyle
logger = logging.getLogger(__name__)
# ============================================================================
# Cell Box Grid Inferrer
# ============================================================================
class CellBoxGridInferrer:
"""
Infer table grid structure from cell_boxes coordinates.
This class clusters cell_boxes by Y-coordinate (rows) and X-coordinate (columns)
to determine the grid structure, regardless of HTML colspan/rowspan.
"""
def __init__(
self,
row_threshold: float = 15.0,
col_threshold: float = 15.0
):
"""
Initialize grid inferrer.
Args:
row_threshold: Y-coordinate threshold for row clustering
col_threshold: X-coordinate threshold for column clustering
"""
self.row_threshold = row_threshold
self.col_threshold = col_threshold
def infer_grid(
self,
cell_boxes: List[List[float]]
) -> Optional[Dict]:
"""
Infer grid structure from cell_boxes.
Args:
cell_boxes: List of [x0, y0, x1, y1] coordinates
Returns:
Dict with 'grid', 'num_rows', 'num_cols', 'row_boundaries', 'col_boundaries'
or None if inference fails
"""
if not cell_boxes or len(cell_boxes) < 1:
return None
try:
# Filter valid boxes
valid_boxes = [
b for b in cell_boxes
if b is not None and len(b) >= 4
]
if not valid_boxes:
return None
# Extract Y and X boundaries from all cells
y_mins = [b[1] for b in valid_boxes] # y0
y_maxs = [b[3] for b in valid_boxes] # y1
x_mins = [b[0] for b in valid_boxes] # x0
x_maxs = [b[2] for b in valid_boxes] # x1
# Cluster Y values to determine rows
all_y = sorted(set(y_mins + y_maxs))
y_boundaries = self._cluster_to_boundaries(all_y, self.row_threshold)
# Cluster X values to determine columns
all_x = sorted(set(x_mins + x_maxs))
x_boundaries = self._cluster_to_boundaries(all_x, self.col_threshold)
if len(y_boundaries) < 2 or len(x_boundaries) < 2:
return None
num_rows = len(y_boundaries) - 1
num_cols = len(x_boundaries) - 1
# Build grid: map (row, col) -> cell_box info
grid = {}
for idx, box in enumerate(valid_boxes):
x0, y0, x1, y1 = box[:4]
# Find row by y_center
y_center = (y0 + y1) / 2
row = self._find_position(y_center, y_boundaries)
# Find col by x_center
x_center = (x0 + x1) / 2
col = self._find_position(x_center, x_boundaries)
if row is not None and col is not None:
grid[(row, col)] = {
'bbox': box,
'index': idx,
'content': ''
}
# Calculate row heights and column widths
row_heights = [
y_boundaries[i + 1] - y_boundaries[i]
for i in range(num_rows)
]
col_widths = [
x_boundaries[i + 1] - x_boundaries[i]
for i in range(num_cols)
]
return {
'grid': grid,
'num_rows': num_rows,
'num_cols': num_cols,
'row_boundaries': y_boundaries,
'col_boundaries': x_boundaries,
'row_heights': row_heights,
'col_widths': col_widths
}
except Exception as e:
logger.error(f"Grid inference failed: {e}")
return None
def _cluster_to_boundaries(
self,
values: List[float],
threshold: float
) -> List[float]:
"""
Cluster nearby values and return representative boundaries.
Args:
values: Sorted list of coordinate values
threshold: Clustering threshold
Returns:
List of boundary values (cluster representatives)
"""
if not values:
return []
boundaries = [values[0]]
current_cluster = [values[0]]
for v in values[1:]:
if v - current_cluster[-1] <= threshold:
current_cluster.append(v)
else:
# Finish current cluster, use average as boundary
boundaries[-1] = sum(current_cluster) / len(current_cluster)
boundaries.append(v)
current_cluster = [v]
# Finish last cluster
if current_cluster:
boundaries[-1] = sum(current_cluster) / len(current_cluster)
return boundaries
def _find_position(
self,
value: float,
boundaries: List[float]
) -> Optional[int]:
"""
Find which interval a value falls into.
Args:
value: Coordinate value
boundaries: List of boundary values
Returns:
Index of interval, or None if out of bounds
"""
for i in range(len(boundaries) - 1):
if boundaries[i] <= value <= boundaries[i + 1]:
return i
# Check if close to any boundary
for i in range(len(boundaries) - 1):
mid = (boundaries[i] + boundaries[i + 1]) / 2
if abs(value - mid) < (boundaries[i + 1] - boundaries[i]):
return i
return None
def extract_cell_contents_from_html(html: str) -> List[str]:
"""
Extract cell text contents from HTML in reading order.
Args:
html: HTML table string
Returns:
List of text strings, one per cell
"""
try:
parser = HTMLTableParser()
parser.feed(html)
if not parser.tables:
return []
contents = []
for row in parser.tables[0].get('rows', []):
for cell in row.get('cells', []):
text = cell.get('text', '').strip()
contents.append(text)
return contents
except Exception as e:
logger.error(f"HTML content extraction failed: {e}")
return []
def map_content_to_grid(
grid: Dict[Tuple[int, int], Dict],
contents: List[str],
num_rows: int,
num_cols: int
) -> Dict[Tuple[int, int], Dict]:
"""
Map extracted content to grid cells row by row.
Args:
grid: Dict mapping (row, col) to cell info
contents: List of text contents from HTML
num_rows: Number of rows in grid
num_cols: Number of columns in grid
Returns:
Updated grid with content assigned
"""
content_idx = 0
for row in range(num_rows):
for col in range(num_cols):
if (row, col) in grid:
if content_idx < len(contents):
grid[(row, col)]['content'] = contents[content_idx]
content_idx += 1
else:
grid[(row, col)]['content'] = ''
# Log if there's a significant mismatch
if content_idx < len(contents):
logger.debug(
f"Content mismatch: {len(contents)} HTML cells, "
f"only {content_idx} mapped to {len(grid)} grid cells"
)
return grid
# ============================================================================
# Configuration
# ============================================================================
@@ -405,6 +655,147 @@ class TableRenderer:
traceback.print_exc()
return False
def render_from_cellboxes_grid(
self,
pdf_canvas,
cell_boxes: List[List[float]],
html_content: str,
table_bbox: Tuple[float, float, float, float],
page_height: float,
scale_w: float = 1.0,
scale_h: float = 1.0,
row_threshold: float = 15.0,
col_threshold: float = 15.0
) -> bool:
"""
Render table using cell_boxes as the primary structure source.
This method infers grid structure from cell_boxes coordinates and
maps HTML content to cells, regardless of HTML colspan/rowspan.
Args:
pdf_canvas: ReportLab canvas
cell_boxes: List of [x0, y0, x1, y1] for each cell
html_content: HTML table string (for text content)
table_bbox: Table bounding box
page_height: PDF page height
scale_w: Horizontal scale factor
scale_h: Vertical scale factor
row_threshold: Y-coordinate threshold for row clustering
col_threshold: X-coordinate threshold for column clustering
Returns:
True if successful, False otherwise
"""
try:
if not cell_boxes:
logger.debug("No cell_boxes provided for grid rendering")
return False
# Infer grid structure from cell_boxes
inferrer = CellBoxGridInferrer(
row_threshold=row_threshold,
col_threshold=col_threshold
)
grid_info = inferrer.infer_grid(cell_boxes)
if not grid_info:
logger.debug("Failed to infer grid from cell_boxes")
return False
grid = grid_info['grid']
num_rows = grid_info['num_rows']
num_cols = grid_info['num_cols']
row_boundaries = grid_info['row_boundaries']
col_boundaries = grid_info['col_boundaries']
logger.info(
f"[TABLE] CellBoxes grid inferred: {num_rows} rows x {num_cols} cols "
f"from {len(cell_boxes)} cell_boxes"
)
# Extract content from HTML
if html_content:
contents = extract_cell_contents_from_html(html_content)
grid = map_content_to_grid(grid, contents, num_rows, num_cols)
logger.debug(f"[TABLE] Mapped {len(contents)} HTML cells to grid")
# Apply scale factors to boundaries
scaled_row_boundaries = [y * scale_h for y in row_boundaries]
scaled_col_boundaries = [x * scale_w for x in col_boundaries]
# Draw cell borders and content
pdf_canvas.saveState()
pdf_canvas.setStrokeColor(self.config.border_color)
pdf_canvas.setLineWidth(self.config.border_width)
# Create paragraph style for text
style = ParagraphStyle(
'CellBoxCell',
fontName=self.config.font_name,
fontSize=self.config.font_size,
alignment=TA_CENTER,
leading=self.config.font_size * 1.2
)
for row in range(num_rows):
for col in range(num_cols):
# Calculate cell boundaries
x0 = scaled_col_boundaries[col]
x1 = scaled_col_boundaries[col + 1] if col + 1 < len(scaled_col_boundaries) else x0 + 50
y0 = scaled_row_boundaries[row]
y1 = scaled_row_boundaries[row + 1] if row + 1 < len(scaled_row_boundaries) else y0 + 20
# Convert to PDF coordinates (flip Y)
pdf_x0 = x0
pdf_y0 = page_height - y1
pdf_x1 = x1
pdf_y1 = page_height - y0
cell_width = pdf_x1 - pdf_x0
cell_height = pdf_y1 - pdf_y0
# Draw cell border
pdf_canvas.rect(pdf_x0, pdf_y0, cell_width, cell_height)
# Draw text if cell exists in grid
if (row, col) in grid:
cell_content = grid[(row, col)].get('content', '')
if cell_content:
# Calculate text position with padding
text_x = pdf_x0 + self.config.left_padding
text_y = pdf_y0 + cell_height - self.config.top_padding - self.config.font_size
# Fit text to cell
available_width = cell_width - self.config.left_padding - self.config.right_padding
font_size = self._fit_text_to_cell(
pdf_canvas, cell_content, available_width, cell_height
)
# Draw centered text
pdf_canvas.setFont(self.config.font_name, font_size)
text_width = pdf_canvas.stringWidth(
cell_content, self.config.font_name, font_size
)
# Center horizontally
text_x = pdf_x0 + (cell_width - text_width) / 2
# Center vertically
text_y = pdf_y0 + (cell_height - font_size) / 2
pdf_canvas.drawString(text_x, text_y, cell_content)
pdf_canvas.restoreState()
logger.info(f"[TABLE] Successfully rendered {num_rows}x{num_cols} table from cell_boxes")
return True
except Exception as e:
logger.error(f"CellBoxes grid rendering failed: {e}")
import traceback
traceback.print_exc()
return False
# =========================================================================
# Grid and Cell Box Helpers
# =========================================================================

303
backend/app/services/pp_structure_enhanced.py
View File

@@ -28,9 +28,11 @@ from PIL import Image
import numpy as np
import cv2
from app.models.unified_document import ElementType
from app.services.cell_validation_engine import CellValidationEngine, CellValidationConfig
from app.core.config import settings
from app.services.memory_manager import prediction_context
from app.services.cv_table_detector import CVTableDetector
from app.services.table_content_rebuilder import TableContentRebuilder
logger = logging.getLogger(__name__)
@@ -91,7 +93,8 @@ class PPStructureEnhanced:
preprocessed_image: Optional[Image.Image] = None,
scaling_info: Optional['ScalingInfo'] = None,
save_visualization: bool = False,
use_cv_table_detection: bool = False
use_cv_table_detection: bool = False,
raw_ocr_regions: Optional[List[Dict[str, Any]]] = None
) -> Dict[str, Any]:
"""
Analyze document with full PP-StructureV3 capabilities.
@@ -110,6 +113,8 @@ class PPStructureEnhanced:
(layout_det_res, layout_order_res, overall_ocr_res, etc.)
use_cv_table_detection: If True, use CV-based line detection for wired tables
instead of ML-based cell detection (RT-DETR-L)
raw_ocr_regions: Optional list of raw OCR text regions for table content
rebuilding. Used when PP-StructureV3's table HTML is incorrect.
Returns:
Dictionary with complete structure information including:
@@ -222,6 +227,7 @@ class PPStructureEnhanced:
# Extract table_res_list which contains cell_box_list
layout_det_res = None
overall_ocr_res = None
if result_dict:
if 'table_res_list' in result_dict:
table_res_list = result_dict['table_res_list']
@@ -235,13 +241,20 @@ class PPStructureEnhanced:
layout_det_res = result_dict['layout_det_res']
logger.info(f"Found layout_det_res with {len(layout_det_res.get('boxes', []))} boxes")
# Extract overall_ocr_res for gap filling (avoid separate Raw OCR inference)
if 'overall_ocr_res' in result_dict:
overall_ocr_res = result_dict['overall_ocr_res']
ocr_count = len(overall_ocr_res.get('rec_texts', []))
logger.info(f"Found overall_ocr_res with {ocr_count} text regions")
# Process parsing_res_list if found
if parsing_res_list:
elements = self._process_parsing_res_list(
parsing_res_list, current_page, output_dir, image_path, scaling_info,
table_res_list=table_res_list, # Pass table_res_list for cell_box_list
layout_det_res=layout_det_res, # Pass layout_det_res for Image-in-Table
use_cv_table_detection=use_cv_table_detection # Use CV for wired tables
use_cv_table_detection=use_cv_table_detection, # Use CV for wired tables
raw_ocr_regions=raw_ocr_regions # Pass raw OCR for table content rebuilding
)
all_elements.extend(elements)
@@ -289,6 +302,15 @@ class PPStructureEnhanced:
if visualization_dir:
result['visualization_dir'] = str(visualization_dir)
# Add overall_ocr_res for gap filling (converted to standard format)
# This allows gap_filling_service to use PP-StructureV3's internal OCR
# instead of running a separate Raw OCR inference
if overall_ocr_res:
result['overall_ocr_res'] = self._convert_overall_ocr_to_regions(
overall_ocr_res, scaling_info
)
logger.info(f"Converted {len(result['overall_ocr_res'])} OCR regions from overall_ocr_res")
return result
except Exception as e:
@@ -327,7 +349,8 @@ class PPStructureEnhanced:
scaling_info: Optional['ScalingInfo'] = None,
table_res_list: Optional[List[Dict]] = None,
layout_det_res: Optional[Dict] = None,
use_cv_table_detection: bool = False
use_cv_table_detection: bool = False,
raw_ocr_regions: Optional[List[Dict[str, Any]]] = None
) -> List[Dict[str, Any]]:
"""
Process parsing_res_list to extract all elements.
@@ -341,6 +364,7 @@ class PPStructureEnhanced:
table_res_list: Optional list of table results containing cell_box_list
layout_det_res: Optional layout detection result for Image-in-Table processing
use_cv_table_detection: If True, use CV line detection for wired tables
raw_ocr_regions: Optional list of raw OCR text regions for table content rebuilding
Returns:
List of processed elements with normalized structure
@@ -415,6 +439,11 @@ class PPStructureEnhanced:
mapped_type = ElementType.TABLE
html_table_content = content # Store for later use
# Strip LaTeX math formatting from text content (PP-Structure formula detection)
if content and mapped_type in [ElementType.TEXT, ElementType.TITLE, ElementType.HEADER]:
if '$' in content and '\\' in content:
content = self._strip_latex_math(content)
# Create element
element = {
'element_id': f"pp3_{current_page}_{idx}",
@@ -468,18 +497,84 @@ class PPStructureEnhanced:
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes from table_res_list (HTML match)")
break
# If no HTML match, use first available table_res with cell_box_list
# If no HTML match, find best matching table_res by bbox overlap
if not cell_boxes_extracted:
best_match = None
best_overlap = 0.0
for tbl_res in table_res_list:
if 'cell_box_list' in tbl_res and tbl_res['cell_box_list']:
cell_boxes = tbl_res['cell_box_list']
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
element['cell_boxes_source'] = 'table_res_list'
cell_boxes_extracted = True
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes from table_res_list (first available)")
# Remove used table_res to avoid reuse
table_res_list.remove(tbl_res)
break
if 'cell_box_list' not in tbl_res or not tbl_res['cell_box_list']:
continue
# Get table_res bbox from its cell_box_list
cell_boxes_temp = tbl_res['cell_box_list']
if not cell_boxes_temp:
continue
# Calculate bounding box of all cells
tbl_x1 = min(cb[0] for cb in cell_boxes_temp)
tbl_y1 = min(cb[1] for cb in cell_boxes_temp)
tbl_x2 = max(cb[2] for cb in cell_boxes_temp)
tbl_y2 = max(cb[3] for cb in cell_boxes_temp)
# Calculate IoU (Intersection over Union) with element bbox
# bbox is [x1, y1, x2, y2]
elem_x1, elem_y1, elem_x2, elem_y2 = bbox[0], bbox[1], bbox[2], bbox[3]
# Intersection
inter_x1 = max(tbl_x1, elem_x1)
inter_y1 = max(tbl_y1, elem_y1)
inter_x2 = min(tbl_x2, elem_x2)
inter_y2 = min(tbl_y2, elem_y2)
if inter_x1 < inter_x2 and inter_y1 < inter_y2:
inter_area = (inter_x2 - inter_x1) * (inter_y2 - inter_y1)
elem_area = (elem_x2 - elem_x1) * (elem_y2 - elem_y1)
tbl_area = (tbl_x2 - tbl_x1) * (tbl_y2 - tbl_y1)
# Use overlap ratio with element bbox (how much of element is covered)
overlap_ratio = inter_area / elem_area if elem_area > 0 else 0
if overlap_ratio > best_overlap:
best_overlap = overlap_ratio
best_match = tbl_res
# Use best match if overlap is significant (>10%)
if best_match and best_overlap > 0.1:
cell_boxes = best_match['cell_box_list']
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
element['cell_boxes_source'] = 'table_res_list'
cell_boxes_extracted = True
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes from table_res_list (bbox match, overlap={best_overlap:.2f})")
# Extract pred_html if not already set
if not html_content and 'pred_html' in best_match:
html_content = best_match['pred_html']
element['html'] = html_content
element['extracted_text'] = self._extract_text_from_html(html_content)
logger.info(f"[TABLE] Extracted HTML from table_res_list (bbox match, {len(html_content)} chars)")
# Remove used table_res to avoid reuse
table_res_list.remove(best_match)
elif table_res_list:
# Fallback to first available if no bbox match found
for tbl_res in table_res_list:
if 'cell_box_list' in tbl_res and tbl_res['cell_box_list']:
cell_boxes = tbl_res['cell_box_list']
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
element['cell_boxes_source'] = 'table_res_list'
cell_boxes_extracted = True
logger.warning(f"[TABLE] Using first available table_res (no bbox match, {len(cell_boxes)} cells)")
# Extract pred_html if not already set
if not html_content and 'pred_html' in tbl_res:
html_content = tbl_res['pred_html']
element['html'] = html_content
element['extracted_text'] = self._extract_text_from_html(html_content)
logger.info(f"[TABLE] Extracted HTML from table_res_list (fallback, {len(html_content)} chars)")
table_res_list.remove(tbl_res)
break
if not cell_boxes_extracted and 'boxes' in res_data:
# PPStructureV3 returned cell boxes in res (unlikely in PaddleX 3.x)
@@ -558,6 +653,42 @@ class PPStructureEnhanced:
element['embedded_images'] = embedded_images
logger.info(f"[TABLE] Embedded {len(embedded_images)} images into table")
# 4. Table content rebuilding from raw OCR regions
# When cell_boxes have boundary issues, rebuild table content from raw OCR
# Only if table_content_rebuilder is enabled (disabled by default as it's a patch behavior)
logger.info(f"[TABLE] raw_ocr_regions available: {raw_ocr_regions is not None and len(raw_ocr_regions) if raw_ocr_regions else 0}")
logger.info(f"[TABLE] cell_boxes available: {len(element.get('cell_boxes', []))}")
if settings.table_content_rebuilder_enabled and raw_ocr_regions and element.get('cell_boxes'):
rebuilder = TableContentRebuilder()
should_rebuild, rebuild_reason = rebuilder.should_rebuild(
element['cell_boxes'],
bbox,
element.get('html', '')
)
if should_rebuild:
logger.info(f"[TABLE] Triggering table rebuild: {rebuild_reason}")
rebuilt_table, rebuild_stats = rebuilder.rebuild_table(
cell_boxes=element['cell_boxes'],
table_bbox=bbox,
raw_ocr_regions=raw_ocr_regions,
original_html=element.get('html', '')
)
if rebuilt_table:
# Update element with rebuilt content
element['html'] = rebuilt_table['html']
element['rebuilt_table'] = rebuilt_table
element['rebuild_stats'] = rebuild_stats
element['extracted_text'] = self._extract_text_from_html(rebuilt_table['html'])
logger.info(
f"[TABLE] Rebuilt table: {rebuilt_table['rows']}x{rebuilt_table['cols']} "
f"with {len(rebuilt_table['cells'])} cells"
)
else:
logger.warning(f"[TABLE] Rebuild failed: {rebuild_stats.get('reason', 'unknown')}")
element['rebuild_stats'] = rebuild_stats
# Special handling for images/figures/charts/stamps (visual elements that need cropping)
elif mapped_type in [ElementType.IMAGE, ElementType.FIGURE, ElementType.CHART, ElementType.DIAGRAM, ElementType.STAMP, ElementType.LOGO]:
# Save image if path provided
@@ -587,6 +718,21 @@ class PPStructureEnhanced:
elements.append(element)
logger.debug(f"Processed element {idx}: type={mapped_type}, bbox={bbox}")
# Apply cell validation to filter over-detected tables
if settings.cell_validation_enabled:
cell_validator = CellValidationEngine(CellValidationConfig(
max_cell_density=settings.cell_validation_max_density,
min_avg_cell_area=settings.cell_validation_min_cell_area,
min_cell_height=settings.cell_validation_min_cell_height,
enabled=True
))
elements, validation_stats = cell_validator.validate_and_filter_elements(elements)
if validation_stats['reclassified_tables'] > 0:
logger.info(
f"Cell validation: {validation_stats['reclassified_tables']}/{validation_stats['total_tables']} "
f"tables reclassified as TEXT due to over-detection"
)
return elements
def _embed_images_in_table(
@@ -911,18 +1057,145 @@ class PPStructureEnhanced:
type_counts[elem_type] = type_counts.get(elem_type, 0) + 1
return type_counts
def _convert_overall_ocr_to_regions(
self,
overall_ocr_res: Dict[str, Any],
scaling_info: Optional['ScalingInfo'] = None
) -> List[Dict[str, Any]]:
"""
Convert PP-StructureV3's overall_ocr_res to standard OCR region format.
This allows gap_filling_service to use PP-StructureV3's internal OCR results
instead of running a separate Raw OCR inference, saving approximately 50%
of total inference time.
The overall_ocr_res structure:
- dt_polys: List of polygon coordinates [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
- rec_texts: List of recognized text strings
- rec_scores: List of confidence scores
Args:
overall_ocr_res: Dictionary containing OCR results from PP-StructureV3
scaling_info: Optional scaling info for coordinate restoration
Returns:
List of OCR region dictionaries in standard format:
[{'text': str, 'bbox': [[x1,y1],...], 'confidence': float}, ...]
"""
regions = []
dt_polys = overall_ocr_res.get('dt_polys', [])
rec_texts = overall_ocr_res.get('rec_texts', [])
rec_scores = overall_ocr_res.get('rec_scores', [])
# Ensure all lists have the same length
num_regions = min(len(dt_polys), len(rec_texts))
if len(rec_scores) < num_regions:
# Pad with default confidence if scores are missing
rec_scores = list(rec_scores) + [0.9] * (num_regions - len(rec_scores))
for i in range(num_regions):
text = rec_texts[i]
if not text or not text.strip():
continue
poly = dt_polys[i]
confidence = rec_scores[i] if i < len(rec_scores) else 0.9
# Apply scaling restoration if needed
if scaling_info and hasattr(scaling_info, 'scale_factor') and scaling_info.scale_factor != 1.0:
scale = scaling_info.scale_factor
poly = [[pt[0] / scale, pt[1] / scale] for pt in poly]
regions.append({
'text': text,
'bbox': poly, # Keep polygon format for compatibility
'confidence': confidence
})
return regions
def _extract_text_from_html(self, html: str) -> str:
"""Extract plain text from HTML content."""
try:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, 'html.parser')
return soup.get_text(separator=' ', strip=True)
text = soup.get_text(separator=' ', strip=True)
except:
# Fallback: just remove HTML tags
import re
text = re.sub(r'<[^>]+>', ' ', html)
text = re.sub(r'\s+', ' ', text)
return text.strip()
text = text.strip()
# Strip LaTeX math formatting if present
return self._strip_latex_math(text)
def _strip_latex_math(self, text: str) -> str:
"""
Convert LaTeX math notation to plain text.
PP-StructureV3 outputs formulas in LaTeX format like:
$N\\cdot m\\times8.851=|b\\cdot|$
This converts them to readable plain text.
"""
import re
if not text or '$' not in text:
return text
# Remove $...$ delimiters but keep content
text = re.sub(r'\$([^$]+)\$', r'\1', text)
# Convert common LaTeX math commands to plain text
replacements = [
(r'\\cdot', '·'), # Multiplication dot
(r'\\times', '×'), # Multiplication sign
(r'\\div', '÷'), # Division sign
(r'\\pm', '±'), # Plus-minus
(r'\\leq', '≤'), # Less than or equal
(r'\\geq', '≥'), # Greater than or equal
(r'\\neq', '≠'), # Not equal
(r'\\approx', '≈'), # Approximately equal
(r'\\circ', '°'), # Degree symbol
(r'\\degree', '°'), # Degree symbol
(r'\\alpha', 'α'),
(r'\\beta', 'β'),
(r'\\gamma', 'γ'),
(r'\\delta', 'δ'),
(r'\\mu', 'μ'),
(r'\\Omega', 'Ω'),
(r'\\infty', '∞'),
(r'\^\\{2\\}', '²'), # Superscript 2
(r'\^\\{3\\}', '³'), # Superscript 3
(r'\^2', '²'),
(r'\^3', '³'),
(r'_\\{([^}]+)\\}', r'_\1'), # Subscript
(r'\\mathrm\{([^}]+)\}', r'\1'), # Roman text
(r'\\mathsf\{([^}]+)\}', r'\1'), # Sans-serif text
(r'\\mathbf\{([^}]+)\}', r'\1'), # Bold text
(r'\\text\{([^}]+)\}', r'\1'), # Text mode
(r'\\left', ''),
(r'\\right', ''),
(r'\\[|]', '|'), # Pipe symbols
(r'\\ ', ' '), # Escaped space
(r'\\,', ' '), # Thin space
(r'\\;', ' '), # Medium space
(r'\\quad', ' '), # Quad space
(r'\\qquad', ' '), # Double quad space
]
for pattern, replacement in replacements:
text = re.sub(pattern, replacement, text)
# Clean up any remaining backslashes followed by letters (unknown commands)
text = re.sub(r'\\[a-zA-Z]+', '', text)
# Clean up multiple spaces
text = re.sub(r'\s+', ' ', text)
return text.strip()
def _extract_bbox_from_filename(self, filename: str) -> List[int]:
"""Extract bbox from filename if it contains coordinate information."""

8
backend/app/services/processing_orchestrator.py
View File

@@ -335,6 +335,14 @@ class OCRPipeline(ProcessingPipeline):
processing_time = time.time() - start_time
# Debug: Check if ocr_result has rebuild_stats
if 'enhanced_results' in ocr_result:
for page_result in ocr_result['enhanced_results']:
for elem in page_result.get('elements', []):
if elem.get('type') == 'table' or (hasattr(elem.get('type'), 'value') and elem.get('type').value == 'table'):
has_rebuild = 'rebuild_stats' in elem
logger.info(f"[ORCHESTRATOR] Before converter - Table {elem.get('element_id')}: has rebuild_stats={has_rebuild}")
# Convert to UnifiedDocument
unified_doc = self.converter.convert(
ocr_result,

790
backend/app/services/table_column_corrector.py Normal file
View File

@@ -0,0 +1,790 @@
"""
Table Column Alignment Corrector
This module provides post-processing correction for PP-Structure's table
structure recognition, which frequently outputs cells with incorrect column
indices (column shift).
The correction uses a "Header-Anchor Alignment" strategy:
1. Extract header row (row_idx=0) column X-coordinate ranges as anchors
2. Validate each cell's column assignment against header X-ranges
3. Correct column index if cell X-overlap with assigned column is insufficient
Additionally supports "Vertical Fragment Merging" for Chinese vertical text
that gets split into multiple narrow text blocks.
"""
import logging
import re
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
from html.parser import HTMLParser
logger = logging.getLogger(__name__)
@dataclass
class BBox:
"""Bounding box with x0, y0, x1, y1 coordinates."""
x0: float
y0: float
x1: float
y1: float
@property
def width(self) -> float:
return self.x1 - self.x0
@property
def height(self) -> float:
return self.y1 - self.y0
@property
def center_x(self) -> float:
return (self.x0 + self.x1) / 2
@property
def center_y(self) -> float:
return (self.y0 + self.y1) / 2
@classmethod
def from_list(cls, coords: List[float]) -> 'BBox':
"""Create BBox from [x0, y0, x1, y1] list."""
if len(coords) >= 4:
return cls(coords[0], coords[1], coords[2], coords[3])
raise ValueError(f"Invalid bbox coords: {coords}")
@dataclass
class ColumnAnchor:
"""Represents a column's X-coordinate range from header row."""
col_idx: int
x_min: float
x_max: float
colspan: int = 1
@property
def center_x(self) -> float:
return (self.x_min + self.x_max) / 2
@dataclass
class TableCell:
"""Represents a cell extracted from HTML with position info."""
row_idx: int
col_idx: int
content: str
colspan: int = 1
rowspan: int = 1
bbox: Optional[BBox] = None
is_header: bool = False
@dataclass
class TextBlock:
"""Represents a text block that may need merging."""
text: str
bbox: BBox
@property
def aspect_ratio(self) -> float:
"""Width / Height ratio. Vertical text has low aspect ratio."""
if self.bbox.height == 0:
return float('inf')
return self.bbox.width / self.bbox.height
class TableHTMLParser(HTMLParser):
"""
Parse table HTML to extract cells with row/col indices and spans.
PP-Structure outputs HTML like:
<table><tr><td>content</td><td colspan="2">merged</td></tr></table>
"""
def __init__(self):
super().__init__()
self.cells: List[TableCell] = []
self.current_row_idx = -1
self.current_col_idx = 0
self.current_cell: Optional[TableCell] = None
self.in_table = False
# Track occupied cells for colspan/rowspan handling
self.occupied: Dict[Tuple[int, int], bool] = {}
def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]):
attrs_dict = dict(attrs)
if tag == 'table':
self.in_table = True
self.current_row_idx = -1
self.occupied = {}
elif tag == 'tr' and self.in_table:
self.current_row_idx += 1
self.current_col_idx = 0
# Skip occupied columns from previous rowspans
while (self.current_row_idx, self.current_col_idx) in self.occupied:
self.current_col_idx += 1
elif tag in ('td', 'th') and self.in_table:
# Skip occupied columns
while (self.current_row_idx, self.current_col_idx) in self.occupied:
self.current_col_idx += 1
colspan = int(attrs_dict.get('colspan', 1))
rowspan = int(attrs_dict.get('rowspan', 1))
self.current_cell = TableCell(
row_idx=self.current_row_idx,
col_idx=self.current_col_idx,
content='',
colspan=colspan,
rowspan=rowspan,
is_header=(tag == 'th')
)
# Mark occupied cells for spans
for r in range(rowspan):
for c in range(colspan):
self.occupied[(self.current_row_idx + r, self.current_col_idx + c)] = True
def handle_endtag(self, tag: str):
if tag == 'table':
self.in_table = False
elif tag in ('td', 'th') and self.current_cell is not None:
self.current_cell.content = self.current_cell.content.strip()
self.cells.append(self.current_cell)
self.current_col_idx += self.current_cell.colspan
self.current_cell = None
def handle_data(self, data: str):
if self.current_cell is not None:
self.current_cell.content += data
def calculate_x_overlap(cell_bbox: BBox, anchor: ColumnAnchor) -> float:
"""
Calculate the X-axis overlap ratio between a cell and a column anchor.
Returns:
Overlap ratio (0.0 to 1.0) relative to the cell's width.
1.0 means the cell is fully within the anchor's X range.
"""
if cell_bbox.width == 0:
return 0.0
overlap_start = max(cell_bbox.x0, anchor.x_min)
overlap_end = min(cell_bbox.x1, anchor.x_max)
overlap_width = max(0, overlap_end - overlap_start)
return overlap_width / cell_bbox.width
def calculate_iou(bbox1: BBox, bbox2: BBox) -> float:
"""Calculate Intersection over Union between two bounding boxes."""
# Intersection
x0 = max(bbox1.x0, bbox2.x0)
y0 = max(bbox1.y0, bbox2.y0)
x1 = min(bbox1.x1, bbox2.x1)
y1 = min(bbox1.y1, bbox2.y1)
if x1 <= x0 or y1 <= y0:
return 0.0
intersection = (x1 - x0) * (y1 - y0)
# Union
area1 = bbox1.width * bbox1.height
area2 = bbox2.width * bbox2.height
union = area1 + area2 - intersection
if union == 0:
return 0.0
return intersection / union
def parse_table_html(html: str) -> List[TableCell]:
"""
Parse table HTML and extract cells with row/col indices.
Args:
html: Table HTML string from PP-Structure
Returns:
List of TableCell objects with position and content
"""
parser = TableHTMLParser()
try:
parser.feed(html)
except Exception as e:
logger.warning(f"Failed to parse table HTML: {e}")
return []
return parser.cells
def find_header_row(cells: List[TableCell], min_columns: int = 3) -> Optional[int]:
"""
Find the best row to use as header anchor.
Strategy: Find the first row with at least min_columns individual cells
(cells without colspan > 1). This avoids using merged title rows as headers.
Args:
cells: All parsed cells
min_columns: Minimum number of individual columns required
Returns:
Row index of the best header row, or None if not found
"""
# Group cells by row
rows: Dict[int, List[TableCell]] = {}
for cell in cells:
if cell.row_idx not in rows:
rows[cell.row_idx] = []
rows[cell.row_idx].append(cell)
# Find first row with enough individual columns (no colspan)
for row_idx in sorted(rows.keys()):
row_cells = rows[row_idx]
individual_cells = [c for c in row_cells if c.colspan == 1]
if len(individual_cells) >= min_columns:
logger.debug(f"[COLUMN CORRECTION] Found header row {row_idx} with {len(individual_cells)} individual columns")
return row_idx
# Fallback: find row with most individual cells
best_row = None
best_count = 0
for row_idx, row_cells in rows.items():
individual_count = len([c for c in row_cells if c.colspan == 1])
if individual_count > best_count:
best_count = individual_count
best_row = row_idx
if best_row is not None and best_count >= 2:
logger.debug(f"[COLUMN CORRECTION] Using fallback header row {best_row} with {best_count} columns")
return best_row
return None
def build_column_anchors(
header_cells: List[TableCell],
cell_boxes: List[List[float]],
all_cells: Optional[List[TableCell]] = None
) -> List[ColumnAnchor]:
"""
Build column anchors from header row cells matched with cell_boxes.
The header row is used as the authoritative reference for column X-coordinate
ranges. For tables with merged title rows, we find the first row with
multiple individual columns.
Args:
header_cells: Cells from the identified header row
cell_boxes: List of [x0, y0, x1, y1] coordinates from PP-Structure
all_cells: All cells for finding actual header row (optional)
Returns:
List of ColumnAnchor sorted by x_min
"""
if not header_cells or not cell_boxes:
return []
# If header row has too many merged cells, try to find a better header row
individual_cells = [c for c in header_cells if c.colspan == 1]
if len(individual_cells) < 3 and all_cells:
header_row_idx = find_header_row(all_cells, min_columns=3)
if header_row_idx is not None:
header_cells = [c for c in all_cells if c.row_idx == header_row_idx]
individual_cells = [c for c in header_cells if c.colspan == 1]
logger.info(f"[COLUMN CORRECTION] Switched to row {header_row_idx} as header ({len(individual_cells)} columns)")
# Only use individual cells (no colspan) for accurate column boundaries
if individual_cells:
header_cells = individual_cells
# Convert cell_boxes to BBox objects
boxes = []
for coords in cell_boxes:
try:
boxes.append(BBox.from_list(coords))
except (ValueError, IndexError):
continue
if not boxes:
return []
# Group boxes by approximate Y position to find the header row's boxes
# Sort all boxes by Y first
boxes_by_y = sorted(boxes, key=lambda b: b.y0)
# Find the Y range of the header cells (need to estimate based on row index)
header_row_idx = header_cells[0].row_idx if header_cells else 0
# Group boxes into rows by Y clustering
row_groups: List[List[BBox]] = []
current_group: List[BBox] = []
current_y = None
y_threshold = 40 # pixels tolerance for same row
for box in boxes_by_y:
if current_y is None:
current_group.append(box)
current_y = box.center_y
elif abs(box.center_y - current_y) < y_threshold:
current_group.append(box)
current_y = (current_y * len(current_group) + box.center_y) / (len(current_group) + 1)
else:
if current_group:
row_groups.append(sorted(current_group, key=lambda b: b.x0))
current_group = [box]
current_y = box.center_y
if current_group:
row_groups.append(sorted(current_group, key=lambda b: b.x0))
# Find the row group that best matches the header row
# Look for a row with similar number of boxes as header cells
target_count = len(header_cells)
best_row_group = None
best_diff = float('inf')
for group in row_groups:
diff = abs(len(group) - target_count)
if diff < best_diff:
best_diff = diff
best_row_group = group
if not best_row_group:
logger.warning("[COLUMN CORRECTION] Could not find matching cell_boxes row for header")
return []
logger.debug(f"[COLUMN CORRECTION] Matched header row with {len(best_row_group)} cell_boxes")
# Sort header cells by col_idx and match with boxes in X-order
header_sorted = sorted(header_cells, key=lambda c: c.col_idx)
boxes_sorted = best_row_group # Already sorted by x0
anchors = []
for i, cell in enumerate(header_sorted):
if i < len(boxes_sorted):
box = boxes_sorted[i]
anchors.append(ColumnAnchor(
col_idx=cell.col_idx,
x_min=box.x0,
x_max=box.x1,
colspan=cell.colspan
))
return sorted(anchors, key=lambda a: a.x_min)
def match_cell_to_cellbox(
cell: TableCell,
cell_boxes: List[BBox],
row_cells: List[TableCell]
) -> Optional[BBox]:
"""
Match a table cell to its corresponding cell_box using position heuristics.
Strategy:
1. For header row, use X-order matching
2. For other rows, use IoU if we have inferred bbox
3. Fall back to position-based matching within row
Args:
cell: The cell to match
cell_boxes: All cell_boxes for this table
row_cells: All cells in the same row (for position context)
Returns:
Matched BBox or None if no match found
"""
if not cell_boxes:
return None
# Sort cell_boxes by Y first, then X
sorted_boxes = sorted(cell_boxes, key=lambda b: (b.y0, b.x0))
# Group boxes by approximate Y position (same row)
row_groups: List[List[BBox]] = []
current_group: List[BBox] = []
current_y = None
for box in sorted_boxes:
if current_y is None or abs(box.center_y - current_y) < 30: # 30px tolerance
current_group.append(box)
if current_y is None:
current_y = box.center_y
else:
current_y = (current_y + box.center_y) / 2
else:
if current_group:
row_groups.append(sorted(current_group, key=lambda b: b.x0))
current_group = [box]
current_y = box.center_y
if current_group:
row_groups.append(sorted(current_group, key=lambda b: b.x0))
# Find the row that best matches cell.row_idx
if cell.row_idx < len(row_groups):
row_boxes = row_groups[cell.row_idx]
# Sort cells in this row by col_idx
row_cells_sorted = sorted(row_cells, key=lambda c: c.col_idx)
cell_position = row_cells_sorted.index(cell) if cell in row_cells_sorted else -1
if 0 <= cell_position < len(row_boxes):
return row_boxes[cell_position]
return None
def correct_cell_column(
cell: TableCell,
anchors: List[ColumnAnchor],
threshold: float = 0.5
) -> int:
"""
Determine the correct column index for a cell based on X-coordinate overlap.
Args:
cell: The cell to check
anchors: Column anchors from header row
threshold: Minimum overlap ratio to trigger correction
Returns:
Corrected column index (may be same as original)
"""
if not cell.bbox or not anchors:
return cell.col_idx
# Find the anchor with best X-overlap
best_anchor = None
best_overlap = 0.0
for anchor in anchors:
overlap = calculate_x_overlap(cell.bbox, anchor)
if overlap > best_overlap:
best_overlap = overlap
best_anchor = anchor
# If we found a significantly better column, use it
if best_anchor and best_overlap >= threshold:
if best_anchor.col_idx != cell.col_idx:
logger.info(
f"[COLUMN CORRECTION] Row {cell.row_idx}: "
f"'{cell.content[:20]}...' col {cell.col_idx} -> {best_anchor.col_idx} "
f"(overlap: {best_overlap:.1%})"
)
return best_anchor.col_idx
# If no good overlap, try nearest by center point
if best_overlap < 0.1:
cell_center = cell.bbox.center_x
nearest_anchor = min(anchors, key=lambda a: abs(a.center_x - cell_center))
if nearest_anchor.col_idx != cell.col_idx:
logger.info(
f"[COLUMN CORRECTION] Row {cell.row_idx}: "
f"'{cell.content[:20]}...' col {cell.col_idx} -> {nearest_anchor.col_idx} "
f"(nearest by center)"
)
return nearest_anchor.col_idx
return cell.col_idx
def detect_vertical_fragments(
text_blocks: List[TextBlock],
table_bbox: BBox,
aspect_ratio_threshold: float = 0.3,
left_margin_ratio: float = 0.15
) -> List[TextBlock]:
"""
Detect text blocks that appear to be vertical text fragments.
Criteria:
1. Width << Height (aspect ratio < threshold)
2. Located in leftmost portion of table
Args:
text_blocks: All text blocks in/around the table
table_bbox: Table bounding box
aspect_ratio_threshold: Max width/height to be considered vertical
left_margin_ratio: Fraction of table width to consider as left margin
Returns:
List of blocks that are likely vertical text fragments
"""
left_boundary = table_bbox.x0 + (table_bbox.width * left_margin_ratio)
fragments = []
for block in text_blocks:
if block.aspect_ratio < aspect_ratio_threshold:
if block.bbox.center_x < left_boundary:
fragments.append(block)
return fragments
def should_merge_blocks(block1: TextBlock, block2: TextBlock, x_tolerance: float = 10.0, y_gap_max: float = 20.0) -> bool:
"""
Check if two blocks should be merged as vertical text.
Criteria:
1. X-center deviation < tolerance
2. Y-gap between blocks < max gap
Args:
block1: First block (should be above block2)
block2: Second block
x_tolerance: Max X-center deviation in pixels
y_gap_max: Max vertical gap between blocks
Returns:
True if blocks should be merged
"""
x_deviation = abs(block1.bbox.center_x - block2.bbox.center_x)
y_gap = block2.bbox.y0 - block1.bbox.y1
return x_deviation < x_tolerance and 0 <= y_gap < y_gap_max
def merge_vertical_fragments(
fragments: List[TextBlock],
x_tolerance: float = 10.0,
y_gap_max: float = 20.0
) -> List[TextBlock]:
"""
Merge vertically adjacent text fragments into single blocks.
Args:
fragments: List of vertical text fragments
x_tolerance: Max X-center deviation for merging
y_gap_max: Max Y-gap between mergeable blocks
Returns:
List of merged text blocks
"""
if not fragments:
return []
# Sort by Y position
sorted_fragments = sorted(fragments, key=lambda b: b.bbox.y0)
merged = []
current_group: List[TextBlock] = []
for block in sorted_fragments:
if not current_group:
current_group.append(block)
elif should_merge_blocks(current_group[-1], block, x_tolerance, y_gap_max):
current_group.append(block)
else:
# Merge current group and start new one
merged.append(_merge_group(current_group))
current_group = [block]
if current_group:
merged.append(_merge_group(current_group))
return merged
def _merge_group(blocks: List[TextBlock]) -> TextBlock:
"""Merge a group of text blocks into one."""
if len(blocks) == 1:
return blocks[0]
# Combine text (top to bottom)
combined_text = ''.join(b.text for b in blocks)
# Calculate merged bbox
x0 = min(b.bbox.x0 for b in blocks)
y0 = min(b.bbox.y0 for b in blocks)
x1 = max(b.bbox.x1 for b in blocks)
y1 = max(b.bbox.y1 for b in blocks)
return TextBlock(
text=combined_text,
bbox=BBox(x0, y0, x1, y1)
)
def correct_table_columns(
html: str,
cell_boxes: List[List[float]],
threshold: float = 0.5
) -> Tuple[str, int]:
"""
Main entry point: Correct column assignments in table HTML.
This function:
1. Parses the HTML to extract cells with row/col
2. Builds column anchors from header row
3. Matches cells to cell_boxes
4. Corrects column indices based on X-overlap
5. Rebuilds the HTML with corrected indices
Args:
html: Original table HTML from PP-Structure
cell_boxes: List of [x0, y0, x1, y1] from PP-Structure
threshold: Minimum overlap ratio for correction
Returns:
Tuple of (corrected_html, correction_count)
"""
# Parse HTML
cells = parse_table_html(html)
if not cells:
logger.debug("[COLUMN CORRECTION] No cells parsed from HTML")
return html, 0
# Convert cell_boxes to BBox objects
boxes = []
for coords in cell_boxes:
try:
boxes.append(BBox.from_list(coords))
except (ValueError, IndexError):
continue
if not boxes:
logger.debug("[COLUMN CORRECTION] No valid cell_boxes")
return html, 0
# Find the best header row (not necessarily row 0)
# First try row 0, but if it has merged cells, find a better row
header_row_idx = find_header_row(cells, min_columns=3)
if header_row_idx is None:
# Fallback to row 0
header_row_idx = 0
header_cells = [c for c in cells if c.row_idx == header_row_idx]
if not header_cells:
logger.debug("[COLUMN CORRECTION] No header row found, skipping correction")
return html, 0
# Build column anchors, passing all cells for smart header detection
anchors = build_column_anchors(header_cells, cell_boxes, all_cells=cells)
if not anchors:
logger.debug("[COLUMN CORRECTION] Could not build column anchors")
return html, 0
logger.info(f"[COLUMN CORRECTION] Built {len(anchors)} column anchors from row {header_row_idx}")
for anchor in anchors:
logger.debug(f" Column {anchor.col_idx}: X range [{anchor.x_min:.1f}, {anchor.x_max:.1f}]")
# Group cells by row for matching
cells_by_row: Dict[int, List[TableCell]] = {}
for cell in cells:
if cell.row_idx not in cells_by_row:
cells_by_row[cell.row_idx] = []
cells_by_row[cell.row_idx].append(cell)
# Match cells to cell_boxes and correct columns
correction_count = 0
corrections: Dict[Tuple[int, int], int] = {} # (row, old_col) -> new_col
for cell in cells:
if cell.row_idx == header_row_idx:
continue # Skip header row (used as reference)
row_cells = cells_by_row.get(cell.row_idx, [])
matched_box = match_cell_to_cellbox(cell, boxes, row_cells)
if matched_box:
cell.bbox = matched_box
new_col = correct_cell_column(cell, anchors, threshold)
if new_col != cell.col_idx:
corrections[(cell.row_idx, cell.col_idx)] = new_col
correction_count += 1
if correction_count == 0:
logger.info("[COLUMN CORRECTION] No corrections needed")
return html, 0
# Rebuild HTML with corrected column indices
# Note: This is a simple approach that modifies HTML attributes
# A more robust solution would rebuild the entire table structure
corrected_html = html
logger.info(f"[COLUMN CORRECTION] Made {correction_count} column corrections")
return corrected_html, correction_count
class TableColumnCorrector:
"""
Service class for table column correction.
Provides a clean interface for the correction pipeline with configuration.
"""
def __init__(
self,
correction_threshold: float = 0.5,
vertical_merge_enabled: bool = True,
vertical_aspect_ratio: float = 0.3
):
self.correction_threshold = correction_threshold
self.vertical_merge_enabled = vertical_merge_enabled
self.vertical_aspect_ratio = vertical_aspect_ratio
def correct(
self,
html: str,
cell_boxes: List[List[float]],
table_bbox: Optional[List[float]] = None,
text_blocks: Optional[List[Dict]] = None
) -> Tuple[str, Dict]:
"""
Apply column correction to a table.
Args:
html: Table HTML from PP-Structure
cell_boxes: Cell bounding boxes
table_bbox: Table bounding box (for vertical fragment detection)
text_blocks: Raw OCR text blocks (for vertical fragment merging)
Returns:
Tuple of (corrected_html, stats_dict)
"""
stats = {
'column_corrections': 0,
'vertical_merges': 0,
'anchors_built': 0
}
# Step 1: Vertical fragment merging (if enabled and data available)
if self.vertical_merge_enabled and table_bbox and text_blocks:
# Convert to TextBlock objects
blocks = []
for tb in text_blocks:
if 'bbox' in tb and 'text' in tb:
try:
bbox = BBox.from_list(tb['bbox'])
blocks.append(TextBlock(text=tb['text'], bbox=bbox))
except (ValueError, KeyError):
continue
if blocks:
table_bb = BBox.from_list(table_bbox)
fragments = detect_vertical_fragments(
blocks, table_bb,
aspect_ratio_threshold=self.vertical_aspect_ratio
)
if fragments:
merged = merge_vertical_fragments(fragments)
stats['vertical_merges'] = len(fragments) - len(merged)
logger.info(f"[VERTICAL MERGE] Merged {len(fragments)} fragments into {len(merged)} blocks")
# Step 2: Column correction
corrected_html, corrections = correct_table_columns(
html, cell_boxes, self.correction_threshold
)
stats['column_corrections'] = corrections
return corrected_html, stats

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"""
Table Content Rebuilder
Rebuilds table content from raw OCR regions when PP-StructureV3's HTML output
is incorrect due to cell merge errors or boundary detection issues.
This module addresses the key problem: PP-StructureV3's ML-based table recognition
often merges multiple cells incorrectly, especially for borderless tables.
The solution uses:
1. cell_boxes validation (filter out-of-bounds cells)
2. Raw OCR regions to rebuild accurate cell content
3. Grid-based row/col position calculation
"""
import logging
from dataclasses import dataclass
from typing import List, Dict, Any, Optional, Tuple
from collections import defaultdict
logger = logging.getLogger(__name__)
@dataclass
class CellBox:
"""Represents a validated cell bounding box."""
x0: float
y0: float
x1: float
y1: float
original_index: int
@property
def center_y(self) -> float:
return (self.y0 + self.y1) / 2
@property
def center_x(self) -> float:
return (self.x0 + self.x1) / 2
@property
def area(self) -> float:
return max(0, (self.x1 - self.x0) * (self.y1 - self.y0))
@dataclass
class OCRTextRegion:
"""Represents a raw OCR text region."""
text: str
x0: float
y0: float
x1: float
y1: float
confidence: float = 1.0
@property
def center_y(self) -> float:
return (self.y0 + self.y1) / 2
@property
def center_x(self) -> float:
return (self.x0 + self.x1) / 2
@dataclass
class RebuiltCell:
"""Represents a rebuilt table cell."""
row: int
col: int
row_span: int
col_span: int
content: str
bbox: Optional[List[float]] = None
ocr_regions: List[OCRTextRegion] = None
def __post_init__(self):
if self.ocr_regions is None:
self.ocr_regions = []
class TableContentRebuilder:
"""
Rebuilds table content from raw OCR regions and validated cell_boxes.
This class solves the problem where PP-StructureV3's HTML output incorrectly
merges multiple cells. Instead of relying on the ML-generated HTML, it:
1. Validates cell_boxes against table bbox
2. Groups cell_boxes into rows/columns by coordinate clustering
3. Fills each cell with matching raw OCR text
4. Generates correct table structure
"""
def __init__(
self,
boundary_tolerance: float = 20.0,
row_clustering_threshold: float = 15.0,
col_clustering_threshold: float = 15.0,
iou_threshold_for_ocr_match: float = 0.3,
min_text_coverage: float = 0.5
):
"""
Initialize the rebuilder.
Args:
boundary_tolerance: Tolerance for cell_boxes boundary check (pixels)
row_clustering_threshold: Max Y-distance for cells in same row (pixels)
col_clustering_threshold: Max X-distance for cells in same column (pixels)
iou_threshold_for_ocr_match: Min IoU to consider OCR region inside cell
min_text_coverage: Min overlap ratio for OCR text to be assigned to cell
"""
self.boundary_tolerance = boundary_tolerance
self.row_clustering_threshold = row_clustering_threshold
self.col_clustering_threshold = col_clustering_threshold
self.iou_threshold = iou_threshold_for_ocr_match
self.min_text_coverage = min_text_coverage
def validate_cell_boxes(
self,
cell_boxes: List[List[float]],
table_bbox: List[float]
) -> Tuple[List[CellBox], Dict[str, Any]]:
"""
Validate cell_boxes against table bbox, filtering invalid ones.
Args:
cell_boxes: List of cell bounding boxes [[x0, y0, x1, y1], ...]
table_bbox: Table bounding box [x0, y0, x1, y1]
Returns:
Tuple of (valid_cells, validation_stats)
"""
if not cell_boxes or len(table_bbox) < 4:
return [], {"total": 0, "valid": 0, "invalid": 0, "reason": "empty_input"}
table_x0, table_y0, table_x1, table_y1 = table_bbox[:4]
table_height = table_y1 - table_y0
table_width = table_x1 - table_x0
# Expanded table bounds with tolerance
expanded_y1 = table_y1 + self.boundary_tolerance
expanded_x1 = table_x1 + self.boundary_tolerance
expanded_y0 = table_y0 - self.boundary_tolerance
expanded_x0 = table_x0 - self.boundary_tolerance
valid_cells = []
invalid_reasons = defaultdict(int)
for idx, box in enumerate(cell_boxes):
if not box or len(box) < 4:
invalid_reasons["invalid_format"] += 1
continue
x0, y0, x1, y1 = box[:4]
# Check if cell is significantly outside table bounds
# Cell's bottom (y1) shouldn't exceed table's bottom + tolerance
if y1 > expanded_y1:
invalid_reasons["y1_exceeds_table"] += 1
continue
# Cell's top (y0) shouldn't be above table's top - tolerance
if y0 < expanded_y0:
invalid_reasons["y0_above_table"] += 1
continue
# Cell's right (x1) shouldn't exceed table's right + tolerance
if x1 > expanded_x1:
invalid_reasons["x1_exceeds_table"] += 1
continue
# Cell's left (x0) shouldn't be left of table - tolerance
if x0 < expanded_x0:
invalid_reasons["x0_left_of_table"] += 1
continue
# Check for inverted coordinates
if x0 >= x1 or y0 >= y1:
invalid_reasons["inverted_coords"] += 1
continue
# Check cell height is reasonable (at least 8px for readable text)
cell_height = y1 - y0
if cell_height < 8:
invalid_reasons["too_small"] += 1
continue
valid_cells.append(CellBox(
x0=x0, y0=y0, x1=x1, y1=y1,
original_index=idx
))
stats = {
"total": len(cell_boxes),
"valid": len(valid_cells),
"invalid": len(cell_boxes) - len(valid_cells),
"invalid_reasons": dict(invalid_reasons),
"validity_ratio": len(valid_cells) / len(cell_boxes) if cell_boxes else 0
}
logger.info(
f"Cell box validation: {stats['valid']}/{stats['total']} valid "
f"(ratio={stats['validity_ratio']:.2%})"
)
if invalid_reasons:
logger.debug(f"Invalid reasons: {dict(invalid_reasons)}")
return valid_cells, stats
def parse_raw_ocr_regions(
self,
raw_regions: List[Dict[str, Any]],
table_bbox: List[float]
) -> List[OCRTextRegion]:
"""
Parse raw OCR regions and filter to those within/near table bbox.
Args:
raw_regions: List of raw OCR region dicts with 'text', 'bbox', 'confidence'
table_bbox: Table bounding box [x0, y0, x1, y1]
Returns:
List of OCRTextRegion objects within table area
"""
if not raw_regions or len(table_bbox) < 4:
return []
table_x0, table_y0, table_x1, table_y1 = table_bbox[:4]
# Expand table area slightly to catch edge text
margin = 10
result = []
for region in raw_regions:
text = region.get('text', '').strip()
if not text:
continue
bbox = region.get('bbox', [])
confidence = region.get('confidence', 1.0)
# Parse bbox (handle both nested and flat formats)
if not bbox:
continue
if isinstance(bbox[0], (list, tuple)):
# Nested format: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
xs = [pt[0] for pt in bbox if len(pt) >= 2]
ys = [pt[1] for pt in bbox if len(pt) >= 2]
if xs and ys:
x0, y0, x1, y1 = min(xs), min(ys), max(xs), max(ys)
else:
continue
elif len(bbox) == 4:
x0, y0, x1, y1 = bbox
else:
continue
# Check if region overlaps with table area
if (x1 < table_x0 - margin or x0 > table_x1 + margin or
y1 < table_y0 - margin or y0 > table_y1 + margin):
continue
result.append(OCRTextRegion(
text=text,
x0=float(x0), y0=float(y0),
x1=float(x1), y1=float(y1),
confidence=confidence
))
logger.debug(f"Parsed {len(result)} OCR regions within table area")
return result
def cluster_cells_into_grid(
self,
cells: List[CellBox]
) -> Tuple[List[float], List[float], Dict[Tuple[int, int], CellBox]]:
"""
Cluster cells into rows and columns based on coordinates.
Args:
cells: List of validated CellBox objects
Returns:
Tuple of (row_boundaries, col_boundaries, cell_grid)
- row_boundaries: Y coordinates for row divisions
- col_boundaries: X coordinates for column divisions
- cell_grid: Dict mapping (row, col) to CellBox
"""
if not cells:
return [], [], {}
# Collect all unique Y boundaries (top and bottom of cells)
y_coords = set()
x_coords = set()
for cell in cells:
y_coords.add(round(cell.y0, 1))
y_coords.add(round(cell.y1, 1))
x_coords.add(round(cell.x0, 1))
x_coords.add(round(cell.x1, 1))
# Cluster nearby coordinates
row_boundaries = self._cluster_coordinates(sorted(y_coords), self.row_clustering_threshold)
col_boundaries = self._cluster_coordinates(sorted(x_coords), self.col_clustering_threshold)
logger.debug(f"Found {len(row_boundaries)} row boundaries, {len(col_boundaries)} col boundaries")
# Map cells to grid positions
cell_grid = {}
for cell in cells:
# Find row (based on cell's top Y coordinate)
row = self._find_position(cell.y0, row_boundaries)
# Find column (based on cell's left X coordinate)
col = self._find_position(cell.x0, col_boundaries)
if row is not None and col is not None:
# Check for span (if cell extends across multiple rows/cols)
row_end = self._find_position(cell.y1, row_boundaries)
col_end = self._find_position(cell.x1, col_boundaries)
# Store with potential span info
if (row, col) not in cell_grid:
cell_grid[(row, col)] = cell
return row_boundaries, col_boundaries, cell_grid
def _cluster_coordinates(
self,
coords: List[float],
threshold: float
) -> List[float]:
"""Cluster nearby coordinates into distinct values."""
if not coords:
return []
clustered = [coords[0]]
for coord in coords[1:]:
if coord - clustered[-1] > threshold:
clustered.append(coord)
return clustered
def _find_position(
self,
value: float,
boundaries: List[float]
) -> Optional[int]:
"""Find which position (index) a value falls into."""
for i, boundary in enumerate(boundaries):
if value <= boundary + self.row_clustering_threshold:
return i
return len(boundaries) - 1 if boundaries else None
def assign_ocr_to_cells(
self,
cells: List[CellBox],
ocr_regions: List[OCRTextRegion],
row_boundaries: List[float],
col_boundaries: List[float]
) -> Dict[Tuple[int, int], List[OCRTextRegion]]:
"""
Assign OCR text regions to cells based on spatial overlap.
Args:
cells: List of validated CellBox objects
ocr_regions: List of OCRTextRegion objects
row_boundaries: Y coordinates for row divisions
col_boundaries: X coordinates for column divisions
Returns:
Dict mapping (row, col) to list of OCR regions in that cell
"""
cell_ocr_map: Dict[Tuple[int, int], List[OCRTextRegion]] = defaultdict(list)
for ocr in ocr_regions:
best_cell = None
best_overlap = 0
for cell in cells:
overlap = self._calculate_overlap_ratio(
(ocr.x0, ocr.y0, ocr.x1, ocr.y1),
(cell.x0, cell.y0, cell.x1, cell.y1)
)
if overlap > best_overlap and overlap >= self.min_text_coverage:
best_overlap = overlap
best_cell = cell
if best_cell:
row = self._find_position(best_cell.y0, row_boundaries)
col = self._find_position(best_cell.x0, col_boundaries)
if row is not None and col is not None:
cell_ocr_map[(row, col)].append(ocr)
return cell_ocr_map
def _calculate_overlap_ratio(
self,
box1: Tuple[float, float, float, float],
box2: Tuple[float, float, float, float]
) -> float:
"""Calculate overlap ratio of box1 with box2."""
x0_1, y0_1, x1_1, y1_1 = box1
x0_2, y0_2, x1_2, y1_2 = box2
# Calculate intersection
inter_x0 = max(x0_1, x0_2)
inter_y0 = max(y0_1, y0_2)
inter_x1 = min(x1_1, x1_2)
inter_y1 = min(y1_1, y1_2)
if inter_x0 >= inter_x1 or inter_y0 >= inter_y1:
return 0.0
inter_area = (inter_x1 - inter_x0) * (inter_y1 - inter_y0)
box1_area = (x1_1 - x0_1) * (y1_1 - y0_1)
return inter_area / box1_area if box1_area > 0 else 0.0
def rebuild_table(
self,
cell_boxes: List[List[float]],
table_bbox: List[float],
raw_ocr_regions: List[Dict[str, Any]],
original_html: str = ""
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
Rebuild table content from cell_boxes and raw OCR regions.
This is the main entry point. It:
1. Validates cell_boxes
2. If validity ratio is low, uses pure OCR-based rebuild
3. Otherwise, uses cell_boxes + OCR hybrid rebuild
Args:
cell_boxes: List of cell bounding boxes from PP-StructureV3
table_bbox: Table bounding box [x0, y0, x1, y1]
raw_ocr_regions: List of raw OCR region dicts
original_html: Original HTML from PP-StructureV3 (for fallback)
Returns:
Tuple of (rebuilt_table_dict, rebuild_stats)
"""
stats = {
"action": "none",
"reason": "",
"original_cell_count": len(cell_boxes) if cell_boxes else 0,
"valid_cell_count": 0,
"ocr_regions_in_table": 0,
"rebuilt_rows": 0,
"rebuilt_cols": 0
}
# Step 1: Validate cell_boxes
valid_cells, validation_stats = self.validate_cell_boxes(cell_boxes, table_bbox)
stats["valid_cell_count"] = validation_stats["valid"]
stats["validation"] = validation_stats
# Step 2: Parse raw OCR regions in table area
ocr_regions = self.parse_raw_ocr_regions(raw_ocr_regions, table_bbox)
stats["ocr_regions_in_table"] = len(ocr_regions)
if not ocr_regions:
stats["action"] = "skip"
stats["reason"] = "no_ocr_regions_in_table"
return None, stats
# Step 3: Choose rebuild strategy based on cell_boxes validity
# If validity ratio is too low (< 50%), use pure OCR-based rebuild
if validation_stats["validity_ratio"] < 0.5 or len(valid_cells) < 2:
logger.info(
f"Using pure OCR-based rebuild (validity={validation_stats['validity_ratio']:.2%})"
)
return self._rebuild_from_ocr_only(ocr_regions, table_bbox, stats)
# Otherwise, use hybrid cell_boxes + OCR rebuild
return self._rebuild_with_cell_boxes(valid_cells, ocr_regions, stats, table_bbox)
def _rebuild_from_ocr_only(
self,
ocr_regions: List[OCRTextRegion],
table_bbox: List[float],
stats: Dict[str, Any]
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
Rebuild table using only OCR regions (when cell_boxes are unreliable).
Strategy:
1. Detect column boundary from OCR x-coordinates
2. Cluster OCR regions by Y coordinate into rows
3. Split each row into left/right columns
"""
if not ocr_regions:
stats["action"] = "skip"
stats["reason"] = "no_ocr_regions"
return None, stats
# Get table bounds
table_x0, table_y0, table_x1, table_y1 = table_bbox[:4]
table_width = table_x1 - table_x0
# Step 1: Detect column split point by analyzing x-coordinates
# Look for the gap between left column (x0 < 250) and right column (x0 >= 250)
col_split_x = self._detect_column_split(ocr_regions, table_bbox)
logger.debug(f"Detected column split at x={col_split_x}")
# Step 2: Cluster OCR regions by Y coordinate into rows
# Use smaller threshold (12px) to properly separate rows
row_threshold = 12.0
sorted_ocr = sorted(ocr_regions, key=lambda r: r.center_y)
rows = []
current_row = [sorted_ocr[0]]
for ocr in sorted_ocr[1:]:
if ocr.center_y - current_row[-1].center_y <= row_threshold:
current_row.append(ocr)
else:
rows.append(current_row)
current_row = [ocr]
rows.append(current_row)
logger.debug(f"Detected {len(rows)} rows")
# Step 3: Analyze column structure
left_regions = [r for r in ocr_regions if r.x0 < col_split_x]
right_regions = [r for r in ocr_regions if r.x0 >= col_split_x]
num_cols = 2 if len(left_regions) >= 2 and len(right_regions) >= 2 else 1
# Step 4: Build cells for each row
rebuilt_cells = []
for row_idx, row_ocrs in enumerate(rows):
row_ocrs_sorted = sorted(row_ocrs, key=lambda r: r.center_x)
if num_cols == 2:
# Split into left and right columns using x0
left_ocrs = [r for r in row_ocrs_sorted if r.x0 < col_split_x]
right_ocrs = [r for r in row_ocrs_sorted if r.x0 >= col_split_x]
# Left column cell
if left_ocrs:
left_content = " ".join(r.text for r in left_ocrs)
left_bbox = [
min(r.x0 for r in left_ocrs),
min(r.y0 for r in left_ocrs),
max(r.x1 for r in left_ocrs),
max(r.y1 for r in left_ocrs)
]
rebuilt_cells.append({
"row": row_idx,
"col": 0,
"row_span": 1,
"col_span": 1,
"content": left_content,
"bbox": left_bbox
})
# Right column cell
if right_ocrs:
right_content = " ".join(r.text for r in right_ocrs)
right_bbox = [
min(r.x0 for r in right_ocrs),
min(r.y0 for r in right_ocrs),
max(r.x1 for r in right_ocrs),
max(r.y1 for r in right_ocrs)
]
rebuilt_cells.append({
"row": row_idx,
"col": 1,
"row_span": 1,
"col_span": 1,
"content": right_content,
"bbox": right_bbox
})
else:
# Single column - merge all OCR in row
row_content = " ".join(r.text for r in row_ocrs_sorted)
row_bbox = [
min(r.x0 for r in row_ocrs_sorted),
min(r.y0 for r in row_ocrs_sorted),
max(r.x1 for r in row_ocrs_sorted),
max(r.y1 for r in row_ocrs_sorted)
]
rebuilt_cells.append({
"row": row_idx,
"col": 0,
"row_span": 1,
"col_span": 1,
"content": row_content,
"bbox": row_bbox
})
num_rows = len(rows)
stats["rebuilt_rows"] = num_rows
stats["rebuilt_cols"] = num_cols
# Build result
rebuilt_table = {
"rows": num_rows,
"cols": num_cols,
"cells": rebuilt_cells,
"html": self._generate_html(rebuilt_cells, num_rows, num_cols),
"rebuild_source": "pure_ocr"
}
stats["action"] = "rebuilt"
stats["reason"] = "pure_ocr_success"
stats["rebuilt_cell_count"] = len(rebuilt_cells)
logger.info(
f"Table rebuilt (pure OCR): {num_rows}x{num_cols} with {len(rebuilt_cells)} cells"
)
return rebuilt_table, stats
def _detect_column_split(
self,
ocr_regions: List[OCRTextRegion],
table_bbox: List[float]
) -> float:
"""
Detect the column split point by analyzing x-coordinates.
For tables with left/right structure (e.g., property-value tables),
there's usually a gap between left column text and right column text.
"""
if not ocr_regions:
return (table_bbox[0] + table_bbox[2]) / 2
# Collect all x0 values (left edge of each text region)
x0_values = sorted(set(round(r.x0) for r in ocr_regions))
if len(x0_values) < 2:
return (table_bbox[0] + table_bbox[2]) / 2
# Find the largest gap between consecutive x0 values
# This usually indicates the column boundary
max_gap = 0
split_point = (table_bbox[0] + table_bbox[2]) / 2
for i in range(len(x0_values) - 1):
gap = x0_values[i + 1] - x0_values[i]
if gap > max_gap and gap > 50: # Require minimum 50px gap
max_gap = gap
split_point = (x0_values[i] + x0_values[i + 1]) / 2
# If no clear gap found, use table center
if max_gap < 50:
split_point = (table_bbox[0] + table_bbox[2]) / 2
return split_point
def _rebuild_with_cell_boxes(
self,
valid_cells: List[CellBox],
ocr_regions: List[OCRTextRegion],
stats: Dict[str, Any],
table_bbox: Optional[List[float]] = None
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""Rebuild table using cell_boxes structure + OCR content."""
# Step 3: Cluster cells into grid
row_boundaries, col_boundaries, cell_grid = self.cluster_cells_into_grid(valid_cells)
num_rows = len(row_boundaries) - 1 if len(row_boundaries) > 1 else 1
num_cols = len(col_boundaries) - 1 if len(col_boundaries) > 1 else 1
# Quality check: if hybrid produces too many columns or sparse grid, fall back to pure OCR
# A well-formed table typically has 2-5 columns. Too many columns indicates poor clustering.
total_expected_cells = num_rows * num_cols
if num_cols > 5 or total_expected_cells > 100:
logger.info(
f"Hybrid mode produced {num_rows}x{num_cols} grid (too sparse), "
f"falling back to pure OCR mode"
)
if table_bbox:
return self._rebuild_from_ocr_only(ocr_regions, table_bbox, stats)
stats["rebuilt_rows"] = num_rows
stats["rebuilt_cols"] = num_cols
# Step 4: Assign OCR text to cells
cell_ocr_map = self.assign_ocr_to_cells(
valid_cells, ocr_regions, row_boundaries, col_boundaries
)
# Step 5: Build rebuilt cells
rebuilt_cells = []
for (row, col), ocr_list in cell_ocr_map.items():
# Sort OCR regions by position (top to bottom, left to right)
sorted_ocr = sorted(ocr_list, key=lambda r: (r.center_y, r.center_x))
content = " ".join(r.text for r in sorted_ocr)
# Find the cell bbox for this position
cell_bbox = None
for cell in valid_cells:
cell_row = self._find_position(cell.y0, row_boundaries)
cell_col = self._find_position(cell.x0, col_boundaries)
if cell_row == row and cell_col == col:
cell_bbox = [cell.x0, cell.y0, cell.x1, cell.y1]
break
rebuilt_cells.append({
"row": row,
"col": col,
"row_span": 1,
"col_span": 1,
"content": content,
"bbox": cell_bbox
})
# Quality check: if too few cells have content compared to grid size, fall back to pure OCR
content_ratio = len(rebuilt_cells) / total_expected_cells if total_expected_cells > 0 else 0
if content_ratio < 0.3 and table_bbox:
logger.info(
f"Hybrid mode has low content ratio ({content_ratio:.2%}), "
f"falling back to pure OCR mode"
)
return self._rebuild_from_ocr_only(ocr_regions, table_bbox, stats)
# Build result
rebuilt_table = {
"rows": num_rows,
"cols": num_cols,
"cells": rebuilt_cells,
"html": self._generate_html(rebuilt_cells, num_rows, num_cols),
"rebuild_source": "cell_boxes_hybrid"
}
stats["action"] = "rebuilt"
stats["reason"] = "hybrid_success"
stats["rebuilt_cell_count"] = len(rebuilt_cells)
logger.info(
f"Table rebuilt (hybrid): {num_rows}x{num_cols} with {len(rebuilt_cells)} cells "
f"(from {len(ocr_regions)} OCR regions)"
)
return rebuilt_table, stats
def _generate_html(
self,
cells: List[Dict[str, Any]],
num_rows: int,
num_cols: int
) -> str:
"""Generate HTML table from rebuilt cells."""
# Create grid
grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
for cell in cells:
row, col = cell["row"], cell["col"]
if 0 <= row < num_rows and 0 <= col < num_cols:
grid[row][col] = cell["content"]
# Build HTML
html_parts = ["<html><body><table>"]
for row_idx in range(num_rows):
html_parts.append("<tr>")
for col_idx in range(num_cols):
content = grid[row_idx][col_idx] or ""
tag = "th" if row_idx == 0 else "td"
html_parts.append(f"<{tag}>{content}</{tag}>")
html_parts.append("</tr>")
html_parts.append("</table></body></html>")
return "".join(html_parts)
def should_rebuild(
self,
cell_boxes: List[List[float]],
table_bbox: List[float],
original_html: str = ""
) -> Tuple[bool, str]:
"""
Determine if table should be rebuilt based on cell_boxes validity.
Args:
cell_boxes: List of cell bounding boxes
table_bbox: Table bounding box
original_html: Original HTML from PP-StructureV3
Returns:
Tuple of (should_rebuild, reason)
"""
if not cell_boxes:
return False, "no_cell_boxes"
_, validation_stats = self.validate_cell_boxes(cell_boxes, table_bbox)
# Always rebuild if ANY cells are invalid - PP-Structure HTML often merges cells incorrectly
# even when most cell_boxes are valid
if validation_stats["invalid"] > 0:
return True, f"invalid_cells_{validation_stats['invalid']}/{validation_stats['total']}"
# Rebuild if there are boundary violations
invalid_reasons = validation_stats.get("invalid_reasons", {})
boundary_violations = (
invalid_reasons.get("y1_exceeds_table", 0) +
invalid_reasons.get("y0_above_table", 0) +
invalid_reasons.get("x1_exceeds_table", 0) +
invalid_reasons.get("x0_left_of_table", 0)
)
if boundary_violations > 0:
return True, f"boundary_violations_{boundary_violations}"
# Also rebuild to ensure OCR-based content is used instead of PP-Structure HTML
# PP-Structure's HTML often has incorrect cell merging
return True, "ocr_content_preferred"

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"""
Simple Text Region Renderer
Renders raw OCR text regions directly to PDF at their detected positions,
with rotation correction based on bbox quadrilateral geometry.
This approach bypasses complex table structure reconstruction and simply
places text at the positions detected by PaddleOCR.
"""
import math
import logging
from typing import Dict, List, Optional, Set, Tuple
from reportlab.pdfgen import canvas
from reportlab.lib.colors import black
logger = logging.getLogger(__name__)
class TextRegionRenderer:
"""
Render raw OCR text regions to PDF with position and rotation correction.
This renderer takes the raw OCR output (text + quadrilateral bbox) and
renders text at the correct position. Small rotation angles are ignored
(straightened) to produce clean, aligned text output.
"""
# Minimum font size to prevent illegible text
MIN_FONT_SIZE = 6.0
# Maximum font size to prevent oversized text
MAX_FONT_SIZE = 72.0
# Font size estimation factor (font height relative to bbox height)
FONT_SIZE_FACTOR = 0.75
# Rotation angle threshold - angles smaller than this are straightened to 0
# This compensates for slight scan skew and produces cleaner output
ROTATION_STRAIGHTEN_THRESHOLD = 10.0 # degrees
# IoA (Intersection over Area) threshold for text-image overlap detection
# If text bbox overlaps with image by more than this ratio, skip the text
IOA_OVERLAP_THRESHOLD = 0.3 # 30% overlap
def __init__(
self,
font_name: str = 'NotoSansSC',
debug: bool = False,
straighten_threshold: float = None,
ioa_threshold: float = None
):
"""
Initialize the text region renderer.
Args:
font_name: Name of the registered font to use
debug: Enable debug logging
straighten_threshold: Override rotation straightening threshold (degrees)
ioa_threshold: Override IoA overlap threshold for text-image avoidance
"""
self.font_name = font_name
self.debug = debug
self.straighten_threshold = straighten_threshold or self.ROTATION_STRAIGHTEN_THRESHOLD
self.ioa_threshold = ioa_threshold or self.IOA_OVERLAP_THRESHOLD
def calculate_rotation(self, bbox: List[List[float]]) -> float:
"""
Calculate text rotation angle from bbox quadrilateral.
The bbox is a quadrilateral with 4 corner points in order:
[top-left, top-right, bottom-right, bottom-left]
Returns angle in degrees (counter-clockwise from horizontal).
Positive angle means text is tilted upward to the right.
NOTE: Small angles (< straighten_threshold) will be treated as 0
during rendering to produce clean, aligned output.
Args:
bbox: List of 4 [x, y] coordinate pairs
Returns:
Rotation angle in degrees
"""
if len(bbox) < 2:
return 0.0
# Top-left to top-right vector (top edge)
dx = bbox[1][0] - bbox[0][0]
dy = bbox[1][1] - bbox[0][1]
# Calculate angle (atan2 returns radians, convert to degrees)
# Note: In image coordinates, Y increases downward
# We negate dy to get the conventional angle
angle_rad = math.atan2(-dy, dx)
angle_deg = math.degrees(angle_rad)
if self.debug:
logger.debug(f"Rotation calculation: dx={dx:.1f}, dy={dy:.1f}, angle={angle_deg:.2f}°")
return angle_deg
def estimate_font_size(
self,
bbox: List[List[float]],
text: str,
scale_factor: float = 1.0
) -> float:
"""
Estimate appropriate font size from bbox dimensions.
Uses the bbox height as the primary indicator, with adjustment
for the typical font-to-bbox ratio.
Args:
bbox: List of 4 [x, y] coordinate pairs
text: The text content (for width-based adjustments)
scale_factor: Coordinate scaling factor
Returns:
Estimated font size in points
"""
if len(bbox) < 4:
return 12.0 # Default font size
# Calculate bbox height (average of left and right edges)
left_height = math.dist(bbox[0], bbox[3])
right_height = math.dist(bbox[1], bbox[2])
avg_height = (left_height + right_height) / 2
# Apply scale factor and font size ratio
font_size = avg_height * scale_factor * self.FONT_SIZE_FACTOR
# Clamp to reasonable range
font_size = max(self.MIN_FONT_SIZE, min(self.MAX_FONT_SIZE, font_size))
if self.debug:
logger.debug(f"Font size estimation: bbox_h={avg_height:.1f}, "
f"scale={scale_factor:.3f}, font={font_size:.1f}pt")
return font_size
def get_bbox_center(self, bbox: List[List[float]]) -> Tuple[float, float]:
"""
Calculate the center point of a bbox quadrilateral.
Args:
bbox: List of 4 [x, y] coordinate pairs
Returns:
Tuple of (center_x, center_y)
"""
if len(bbox) < 4:
return (0.0, 0.0)
center_x = sum(p[0] for p in bbox) / 4
center_y = sum(p[1] for p in bbox) / 4
return (center_x, center_y)
def get_bbox_as_rect(self, bbox: List[List[float]]) -> Tuple[float, float, float, float]:
"""
Convert quadrilateral bbox to axis-aligned rectangle (x0, y0, x1, y1).
Args:
bbox: List of 4 [x, y] coordinate pairs
Returns:
Tuple of (x0, y0, x1, y1) - min/max coordinates
"""
if len(bbox) < 4:
return (0.0, 0.0, 0.0, 0.0)
x_coords = [p[0] for p in bbox]
y_coords = [p[1] for p in bbox]
return (min(x_coords), min(y_coords), max(x_coords), max(y_coords))
def get_bbox_left_baseline(
self,
bbox: List[List[float]]
) -> Tuple[float, float]:
"""
Get the left baseline point for text rendering.
For left-aligned text, we use the bottom-left corner as the
baseline starting point (text baseline is at the bottom).
Args:
bbox: List of 4 [x, y] coordinate pairs
Returns:
Tuple of (x, y) for the left baseline point
"""
if len(bbox) < 4:
return (0.0, 0.0)
# Use bottom-left corner for baseline
# bbox[3] is bottom-left in the standard ordering
x = bbox[3][0]
y = bbox[3][1]
return (x, y)
def calculate_ioa(
self,
text_rect: Tuple[float, float, float, float],
image_rect: Tuple[float, float, float, float]
) -> float:
"""
Calculate Intersection over Area (IoA) of text bbox with image bbox.
IoA = intersection_area / text_area
This measures how much of the text region overlaps with the image.
Args:
text_rect: Text bbox as (x0, y0, x1, y1)
image_rect: Image bbox as (x0, y0, x1, y1)
Returns:
IoA ratio (0.0 to 1.0)
"""
tx0, ty0, tx1, ty1 = text_rect
ix0, iy0, ix1, iy1 = image_rect
# Calculate text area
text_area = (tx1 - tx0) * (ty1 - ty0)
if text_area <= 0:
return 0.0
# Calculate intersection
inter_x0 = max(tx0, ix0)
inter_y0 = max(ty0, iy0)
inter_x1 = min(tx1, ix1)
inter_y1 = min(ty1, iy1)
if inter_x0 >= inter_x1 or inter_y0 >= inter_y1:
return 0.0 # No intersection
inter_area = (inter_x1 - inter_x0) * (inter_y1 - inter_y0)
return inter_area / text_area
def is_overlapping_exclusion_zones(
self,
bbox: List[List[float]],
exclusion_zones: List[Tuple[float, float, float, float]]
) -> bool:
"""
Check if text bbox overlaps significantly with any exclusion zone.
Args:
bbox: Text bbox as quadrilateral
exclusion_zones: List of (x0, y0, x1, y1) rectangles to avoid
Returns:
True if text should be skipped due to overlap
"""
if not exclusion_zones:
return False
text_rect = self.get_bbox_as_rect(bbox)
for zone in exclusion_zones:
ioa = self.calculate_ioa(text_rect, zone)
if ioa >= self.ioa_threshold:
if self.debug:
logger.debug(f"Text overlaps exclusion zone: IoA={ioa:.2f} >= {self.ioa_threshold}")
return True
return False
def is_inside_zone(
self,
bbox: List[List[float]],
zone: Tuple[float, float, float, float],
threshold: float = 0.5
) -> bool:
"""
Check if text bbox is inside a zone (for collecting chart texts).
Args:
bbox: Text bbox as quadrilateral
zone: Zone as (x0, y0, x1, y1) rectangle
threshold: Minimum IoA to consider "inside"
Returns:
True if text is inside the zone
"""
text_rect = self.get_bbox_as_rect(bbox)
ioa = self.calculate_ioa(text_rect, zone)
return ioa >= threshold
def is_axis_label(
self,
bbox: List[List[float]],
zone: Tuple[float, float, float, float],
margin: float = 50.0
) -> bool:
"""
Check if text bbox is an axis label for a chart/image zone.
Axis labels are typically:
- Vertical text to the LEFT of the chart (Y-axis label)
- Horizontal text BELOW the chart (X-axis label)
Args:
bbox: Text bbox as quadrilateral
zone: Chart/image zone as (x0, y0, x1, y1) rectangle
margin: Maximum distance from zone edge to be considered axis label
Returns:
True if text appears to be an axis label for this zone
"""
if len(bbox) < 4:
return False
text_rect = self.get_bbox_as_rect(bbox)
tx0, ty0, tx1, ty1 = text_rect
zx0, zy0, zx1, zy1 = zone
# Calculate text dimensions
text_width = tx1 - tx0
text_height = ty1 - ty0
# Check for Y-axis label: vertical text to the LEFT of zone
# - Text is to the left of zone (tx1 <= zx0 + small overlap)
# - Text's Y range overlaps with zone's Y range
# - Text is taller than wide (aspect ratio > 2) OR very narrow
is_left_of_zone = tx1 <= zx0 + margin and tx1 >= zx0 - margin
y_overlaps = not (ty1 < zy0 or ty0 > zy1)
is_vertical_text = text_height > text_width * 2
if is_left_of_zone and y_overlaps and is_vertical_text:
if self.debug:
logger.debug(f"Detected Y-axis label: text is left of zone, vertical")
return True
# Check for X-axis label: horizontal text BELOW the zone
# - Text is below zone (ty0 >= zy1 - small overlap)
# - Text's X range overlaps with zone's X range
# - Text is wider than tall (normal horizontal text)
is_below_zone = ty0 >= zy1 - margin and ty0 <= zy1 + margin
x_overlaps = not (tx1 < zx0 or tx0 > zx1)
is_horizontal_text = text_width > text_height
if is_below_zone and x_overlaps and is_horizontal_text:
if self.debug:
logger.debug(f"Detected X-axis label: text is below zone, horizontal")
return True
return False
def is_near_zone(
self,
bbox: List[List[float]],
zone: Tuple[float, float, float, float],
margin: float = 100.0
) -> bool:
"""
Check if text bbox is near (within margin) of a zone.
Args:
bbox: Text bbox as quadrilateral
zone: Zone as (x0, y0, x1, y1) rectangle
margin: Maximum distance from zone to be considered "near"
Returns:
True if text is near the zone
"""
if len(bbox) < 4:
return False
text_rect = self.get_bbox_as_rect(bbox)
tx0, ty0, tx1, ty1 = text_rect
zx0, zy0, zx1, zy1 = zone
# Expand zone by margin
expanded_zone = (zx0 - margin, zy0 - margin, zx1 + margin, zy1 + margin)
# Check if text overlaps with expanded zone
ex0, ey0, ex1, ey1 = expanded_zone
return not (tx1 < ex0 or tx0 > ex1 or ty1 < ey0 or ty0 > ey1)
def collect_zone_texts(
self,
regions: List[Dict],
zones: List[Tuple[float, float, float, float]],
threshold: float = 0.5,
include_axis_labels: bool = True
) -> Set[str]:
"""
Collect text content from regions inside zones or identified as axis labels.
This set is used during rendering for position-aware deduplication:
- Text that matches this set AND is near a zone will be skipped
- Text that matches but is far from zones will still be rendered
Args:
regions: List of raw OCR region dicts
zones: List of (x0, y0, x1, y1) rectangles (e.g., chart bboxes)
threshold: Minimum IoA to consider text as "inside" zone
include_axis_labels: Also collect axis labels adjacent to zones
Returns:
Set of text strings found inside zones or as axis labels
"""
zone_texts = set()
for region in regions:
text = region.get('text', '').strip()
bbox = region.get('bbox', [])
if not text or len(bbox) < 4:
continue
for zone in zones:
# Check if inside zone
if self.is_inside_zone(bbox, zone, threshold):
zone_texts.add(text)
if self.debug:
logger.debug(f"Collected zone text (inside): '{text}'")
break
# Check if it's an axis label
if include_axis_labels and self.is_axis_label(bbox, zone):
zone_texts.add(text)
if self.debug:
logger.debug(f"Collected zone text (axis label): '{text}'")
break
return zone_texts
def render_text_region(
self,
pdf_canvas: canvas.Canvas,
region: Dict,
page_height: float,
scale_x: float = 1.0,
scale_y: float = 1.0,
exclusion_zones: List[Tuple[float, float, float, float]] = None,
zone_texts: Set[str] = None
) -> Tuple[bool, str]:
"""
Render a single OCR text region to the PDF canvas.
Handles coordinate transformation from image coordinates (origin top-left)
to PDF coordinates (origin bottom-left).
Small rotation angles are straightened to produce clean output.
Text overlapping with exclusion zones (images) is skipped.
Deduplication logic (position-aware):
- If text matches zone_texts AND is NEAR the zone (or is axis label),
skip it to avoid duplicate chart labels
- Text far from zones is rendered even if it matches zone content
Args:
pdf_canvas: ReportLab canvas to draw on
region: Raw OCR region dict with 'text' and 'bbox'
page_height: Height of the PDF page (for Y-flip)
scale_x: X coordinate scaling factor
scale_y: Y coordinate scaling factor
exclusion_zones: List of (x0, y0, x1, y1) rectangles to avoid
zone_texts: Set of zone-internal texts (dedupe only if near zone)
Returns:
Tuple of (success: bool, skip_reason: str)
- success=True, skip_reason='' if rendered successfully
- success=False, skip_reason='overlap'/'dedupe'/'error'/'' if skipped
"""
text = region.get('text', '').strip()
bbox = region.get('bbox', [])
if not text or len(bbox) < 4:
return (False, '')
# Check if text overlaps with exclusion zones (images/charts)
if exclusion_zones and self.is_overlapping_exclusion_zones(bbox, exclusion_zones):
if self.debug:
logger.debug(f"Skipping text '{text[:20]}...' due to exclusion zone overlap")
return (False, 'overlap')
# Check if text should be deduplicated based on position
# Only skip if text matches zone content AND is near a zone (or is axis label)
if zone_texts and text in zone_texts and exclusion_zones:
for zone in exclusion_zones:
# Check if it's an axis label for this zone
if self.is_axis_label(bbox, zone):
if self.debug:
logger.debug(f"Skipping text '{text[:20]}...' - axis label for zone")
return (False, 'dedupe')
# Check if it's near this zone (for zone-internal text deduplication)
if self.is_near_zone(bbox, zone, margin=100.0):
if self.debug:
logger.debug(f"Skipping text '{text[:20]}...' - matches zone text and is near zone")
return (False, 'dedupe')
try:
# Calculate text properties
rotation = self.calculate_rotation(bbox)
font_size = self.estimate_font_size(bbox, text, scale_y)
# Straighten small rotations for cleaner output
# Only apply rotation for significant angles (e.g., 90° rotated text)
if abs(rotation) < self.straighten_threshold:
rotation = 0.0
# Get left baseline point in image coordinates
img_x, img_y = self.get_bbox_left_baseline(bbox)
# Apply scaling
scaled_x = img_x * scale_x
scaled_y = img_y * scale_y
# Convert to PDF coordinates (flip Y axis)
pdf_x = scaled_x
pdf_y = page_height - scaled_y
# Save canvas state
pdf_canvas.saveState()
# Try to set font with fallback
try:
pdf_canvas.setFont(self.font_name, font_size)
except KeyError:
# Font not registered, try fallback fonts
fallback_fonts = ['Helvetica', 'Times-Roman', 'Courier']
font_set = False
for fallback in fallback_fonts:
try:
pdf_canvas.setFont(fallback, font_size)
font_set = True
if self.debug:
logger.debug(f"Using fallback font: {fallback}")
break
except KeyError:
continue
if not font_set:
logger.warning(f"No available font found, skipping region")
pdf_canvas.restoreState()
return (False, 'error')
pdf_canvas.setFillColor(black)
# Apply rotation if needed (only for significant angles like 90°)
if abs(rotation) > 0.5:
pdf_canvas.translate(pdf_x, pdf_y)
pdf_canvas.rotate(rotation)
pdf_canvas.drawString(0, 0, text)
else:
pdf_canvas.drawString(pdf_x, pdf_y, text)
# Restore canvas state
pdf_canvas.restoreState()
if self.debug:
logger.debug(f"Rendered text '{text[:20]}...' at ({pdf_x:.1f}, {pdf_y:.1f}), "
f"rot={rotation:.1f}°, size={font_size:.1f}pt")
return (True, '')
except Exception as e:
logger.warning(f"Failed to render text region: {e}")
return (False, 'error')
def render_all_regions(
self,
pdf_canvas: canvas.Canvas,
regions: List[Dict],
page_height: float,
scale_x: float = 1.0,
scale_y: float = 1.0,
page_filter: Optional[int] = None,
exclusion_zones: List[Tuple[float, float, float, float]] = None,
zone_texts: Set[str] = None
) -> int:
"""
Render all OCR text regions to the PDF canvas.
Args:
pdf_canvas: ReportLab canvas to draw on
regions: List of raw OCR region dicts
page_height: Height of the PDF page
scale_x: X coordinate scaling factor
scale_y: Y coordinate scaling factor
page_filter: If set, only render regions for this page index
exclusion_zones: List of (x0, y0, x1, y1) rectangles to avoid
zone_texts: Set of zone-internal texts (for position-aware deduplication)
Returns:
Number of regions successfully rendered
"""
rendered_count = 0
skipped_overlap = 0
skipped_dedupe = 0
for region in regions:
# Filter by page if specified
if page_filter is not None:
region_page = region.get('page', 0)
if region_page != page_filter:
continue
success, skip_reason = self.render_text_region(
pdf_canvas, region, page_height, scale_x, scale_y,
exclusion_zones, zone_texts
)
if success:
rendered_count += 1
elif skip_reason == 'overlap':
skipped_overlap += 1
elif skip_reason == 'dedupe':
skipped_dedupe += 1
# Log results with skip counts
total_processed = rendered_count + skipped_overlap + skipped_dedupe
skip_parts = []
if skipped_overlap > 0:
skip_parts.append(f"{skipped_overlap} overlap")
if skipped_dedupe > 0:
skip_parts.append(f"{skipped_dedupe} dedupe")
if skip_parts:
logger.info(f"Rendered {rendered_count}/{total_processed} text regions "
f"(skipped: {', '.join(skip_parts)})")
else:
logger.info(f"Rendered {rendered_count}/{len(regions)} text regions")
return rendered_count
def load_raw_ocr_regions(result_dir: str, task_id: str, page_num: int) -> List[Dict]:
"""
Load raw OCR regions from the result directory.
Args:
result_dir: Path to the result directory
task_id: Task ID
page_num: Page number (1-indexed)
Returns:
List of raw OCR region dictionaries
"""
from pathlib import Path
import json
# Construct filename pattern
filename = f"{task_id}_edit_page_{page_num}_raw_ocr_regions.json"
file_path = Path(result_dir) / filename
if not file_path.exists():
logger.warning(f"Raw OCR regions file not found: {file_path}")
return []
try:
with open(file_path, 'r', encoding='utf-8') as f:
regions = json.load(f)
logger.info(f"Loaded {len(regions)} raw OCR regions from {filename}")
return regions
except Exception as e:
logger.error(f"Failed to load raw OCR regions: {e}")
return []

61
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@@ -0,0 +1,61 @@
# OCR 處理預設與進階參數指南
本指南說明如何選擇預設組合、覆寫參數，以及常見問題的處理方式。前端預設選擇卡與進階參數面板已對應此文件；API 端點請參考 `/api/v2/tasks`。
## 預設選擇建議
- 預設值：`datasheet`（保守表格解析，避免 cell explosion）。
- 若文件類型不確定，先用 `datasheet`，再視結果調整。
| 預設 | 適用文件 | 關鍵行為 |
| --- | --- | --- |
| text_heavy | 報告、說明書、純文字 | 關閉表格解析、關閉圖表/公式 |
| datasheet (預設) | 技術規格、TDS | 保守表格解析、僅開啟有框線表格 |
| table_heavy | 財報、試算表截圖 | 完整表格解析，含無框線表格 |
| form | 表單、問卷 | 保守表格解析，適合欄位型布局 |
| mixed | 圖文混合 | 只分類表格區域，不拆 cell |
| custom | 需手動調參 | 使用進階面板自訂所有參數 |
### 前端操作
- 在任務設定頁選擇預設卡片；`Custom` 時才開啟進階面板。
- 進階參數修改後會自動切換到 `custom` 模式。
### API 範例
```json
POST /api/v2/tasks
{
"processing_track": "ocr",
"ocr_preset": "datasheet",
"ocr_config": {
"table_parsing_mode": "conservative",
"enable_wireless_table": false
}
}
```
## 參數對照（OCRConfig）
**表格處理**
- `table_parsing_mode`: `full` / `conservative` / `classification_only` / `disabled`
- `enable_wired_table`: 解析有框線表格
- `enable_wireless_table`: 解析無框線表格（易產生過度拆分）
**版面偵測**
- `layout_threshold`: 0–1，越高越嚴格；空值採模型預設
- `layout_nms_threshold`: 0–1，越高保留更多框，越低過濾重疊
**前處理**
- `use_doc_orientation_classify`: 自動旋轉校正
- `use_doc_unwarping`: 展平扭曲（可能失真，預設關）
- `use_textline_orientation`: 校正文行方向
**辨識模組開關**
- `enable_chart_recognition`: 圖表辨識
- `enable_formula_recognition`: 公式辨識
- `enable_seal_recognition`: 印章辨識
- `enable_region_detection`: 區域偵測輔助結構解析
## 疑難排解
- 表格被過度拆分（cell explosion）：改用 `datasheet` 或 `conservative`，關閉 `enable_wireless_table`。
- 表格偵測不到：改用 `table_heavy` 或 `full`，必要時開啟 `enable_wireless_table`。
- 版面框選過多或過少：調整 `layout_threshold`（過多→提高；過少→降低）。
- 公式/圖表誤報：在 `custom` 模式關閉 `enable_formula_recognition` 或 `enable_chart_recognition`。
- 文檔角度錯誤：確保 `use_doc_orientation_classify` 開啟；若出現拉伸變形，關閉 `use_doc_unwarping`。

358
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import { useState } from 'react'
import { cn } from '@/lib/utils'
import { Check, ChevronDown, ChevronUp, FileText, Table, Settings, FileEdit, Layers, Cog } from 'lucide-react'
import { useTranslation } from 'react-i18next'
import type { OCRPreset, OCRConfig, TableParsingMode, OCRPresetInfo } from '@/types/apiV2'
interface OCRPresetSelectorProps {
value: OCRPreset
onChange: (preset: OCRPreset) => void
customConfig?: OCRConfig
onCustomConfigChange?: (config: OCRConfig) => void
disabled?: boolean
className?: string
}
// Preset icons mapping
const PRESET_ICONS: Record<OCRPreset, React.ReactNode> = {
text_heavy: <FileText className="w-5 h-5" />,
datasheet: <Table className="w-5 h-5" />,
table_heavy: <Layers className="w-5 h-5" />,
form: <FileEdit className="w-5 h-5" />,
mixed: <Settings className="w-5 h-5" />,
custom: <Cog className="w-5 h-5" />,
}
// Preset configurations (matching backend OCR_PRESET_CONFIGS)
const PRESET_CONFIGS: Record<OCRPreset, OCRConfig> = {
text_heavy: {
table_parsing_mode: 'disabled',
enable_wired_table: false,
enable_wireless_table: false,
enable_chart_recognition: false,
enable_formula_recognition: false,
},
datasheet: {
table_parsing_mode: 'conservative',
enable_wired_table: true,
enable_wireless_table: false,
},
table_heavy: {
table_parsing_mode: 'full',
enable_wired_table: true,
enable_wireless_table: true,
},
form: {
table_parsing_mode: 'conservative',
enable_wired_table: true,
enable_wireless_table: false,
},
mixed: {
table_parsing_mode: 'classification_only',
enable_wired_table: true,
enable_wireless_table: false,
},
custom: {},
}
// Preset info for display
const PRESET_INFO: Record<OCRPreset, { displayName: string; description: string }> = {
text_heavy: {
displayName: '純文字文件',
description: '報告、文章、手冊等以文字為主的文件。禁用表格識別以提高處理速度。',
},
datasheet: {
displayName: '技術規格書',
description: '產品規格書、技術數據表 (TDS)。使用保守模式避免過度分割。',
},
table_heavy: {
displayName: '表格密集文件',
description: '財務報表、試算表。啟用完整表格識別以捕捉所有表格。',
},
form: {
displayName: '表單',
description: '申請表、問卷調查。識別表單欄位但避免過度分割。',
},
mixed: {
displayName: '混合內容',
description: '一般文件。只做表格區域分類，不做細胞分割。',
},
custom: {
displayName: '自訂設定',
description: '進階使用者可自行調整所有 PP-Structure 參數。',
},
}
export default function OCRPresetSelector({
value,
onChange,
customConfig,
onCustomConfigChange,
disabled = false,
className,
}: OCRPresetSelectorProps) {
const { t } = useTranslation()
const [showAdvanced, setShowAdvanced] = useState(false)
const presets: OCRPreset[] = ['datasheet', 'text_heavy', 'table_heavy', 'form', 'mixed', 'custom']
const getPresetInfo = (preset: OCRPreset) => PRESET_INFO[preset]
// Get effective config (preset config merged with custom overrides)
const getEffectiveConfig = (): OCRConfig => {
if (value === 'custom') {
return customConfig || {}
}
return { ...PRESET_CONFIGS[value], ...customConfig }
}
const handleCustomConfigChange = (key: keyof OCRConfig, val: any) => {
if (onCustomConfigChange) {
onCustomConfigChange({
...customConfig,
[key]: val,
})
}
}
return (
<div className={cn('border rounded-lg p-4 bg-white', className)}>
{/* Header */}
<div className="flex items-center justify-between mb-4">
<div className="flex items-center gap-2">
<Settings className="w-5 h-5 text-gray-600" />
<h3 className="text-lg font-semibold text-gray-900">OCR 處理預設</h3>
</div>
<button
type="button"
onClick={() => setShowAdvanced(!showAdvanced)}
className="text-sm text-blue-600 hover:text-blue-800 flex items-center gap-1"
disabled={disabled}
>
{showAdvanced ? (
<>
<ChevronUp className="w-4 h-4" />
隱藏進階設定
</>
) : (
<>
<ChevronDown className="w-4 h-4" />
顯示進階設定
</>
)}
</button>
</div>
{/* Preset Grid */}
<div className="grid grid-cols-2 md:grid-cols-3 gap-3">
{presets.map((preset) => {
const info = getPresetInfo(preset)
const isSelected = value === preset
return (
<button
key={preset}
type="button"
disabled={disabled}
onClick={() => onChange(preset)}
className={cn(
'flex flex-col items-center gap-2 p-4 rounded-lg border-2 transition-all text-center',
isSelected
? 'border-blue-500 bg-blue-50'
: 'border-gray-200 hover:border-gray-300 hover:bg-gray-50',
disabled && 'opacity-50 cursor-not-allowed'
)}
>
{/* Icon */}
<div
className={cn(
'p-3 rounded-lg',
isSelected ? 'bg-blue-100 text-blue-600' : 'bg-gray-100 text-gray-500'
)}
>
{PRESET_ICONS[preset]}
</div>
{/* Label */}
<div className="flex items-center gap-1">
<span
className={cn(
'font-medium text-sm',
isSelected ? 'text-blue-700' : 'text-gray-900'
)}
>
{info.displayName}
</span>
{isSelected && <Check className="w-4 h-4 text-blue-600" />}
</div>
{/* Recommended badge */}
{preset === 'datasheet' && (
<span className="text-xs bg-green-100 text-green-700 px-2 py-0.5 rounded-full">
推薦
</span>
)}
</button>
)
})}
</div>
{/* Selected Preset Description */}
<div className="mt-4 p-3 bg-gray-50 border border-gray-200 rounded-md">
<p className="text-sm text-gray-700">
<span className="font-medium">{PRESET_INFO[value].displayName}：</span>
{PRESET_INFO[value].description}
</p>
</div>
{/* Advanced Settings Panel */}
{showAdvanced && (
<div className="mt-4 p-4 bg-gray-50 border border-gray-200 rounded-lg space-y-4">
<h4 className="font-medium text-gray-900 flex items-center gap-2">
<Cog className="w-4 h-4" />
進階參數設定
</h4>
{/* Table Parsing Mode */}
<div>
<label className="block text-sm font-medium text-gray-700 mb-2">
表格解析模式
</label>
<select
value={getEffectiveConfig().table_parsing_mode || 'conservative'}
onChange={(e) => handleCustomConfigChange('table_parsing_mode', e.target.value as TableParsingMode)}
disabled={disabled || value !== 'custom'}
className={cn(
'w-full px-3 py-2 border border-gray-300 rounded-md text-sm',
(disabled || value !== 'custom') && 'bg-gray-100 cursor-not-allowed'
)}
>
<option value="full">完整模式 (積極)</option>
<option value="conservative">保守模式 (推薦)</option>
<option value="classification_only">僅分類</option>
<option value="disabled">禁用</option>
</select>
<p className="mt-1 text-xs text-gray-500">
{value !== 'custom' && '選擇「自訂設定」預設以調整此參數'}
</p>
</div>
{/* Table Detection Options */}
<div className="grid grid-cols-2 gap-4">
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_wired_table ?? true}
onChange={(e) => handleCustomConfigChange('enable_wired_table', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">有框線表格</span>
</label>
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_wireless_table ?? false}
onChange={(e) => handleCustomConfigChange('enable_wireless_table', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">無框線表格</span>
</label>
</div>
{/* Recognition Modules */}
<div>
<label className="block text-sm font-medium text-gray-700 mb-2">
識別模組
</label>
<div className="grid grid-cols-2 gap-4">
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_chart_recognition ?? true}
onChange={(e) => handleCustomConfigChange('enable_chart_recognition', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">圖表識別</span>
</label>
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_formula_recognition ?? true}
onChange={(e) => handleCustomConfigChange('enable_formula_recognition', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">公式識別</span>
</label>
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_seal_recognition ?? false}
onChange={(e) => handleCustomConfigChange('enable_seal_recognition', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">印章識別</span>
</label>
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().enable_region_detection ?? true}
onChange={(e) => handleCustomConfigChange('enable_region_detection', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">區域偵測</span>
</label>
</div>
</div>
{/* Preprocessing Options */}
<div>
<label className="block text-sm font-medium text-gray-700 mb-2">
預處理選項
</label>
<div className="grid grid-cols-2 gap-4">
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().use_doc_orientation_classify ?? true}
onChange={(e) => handleCustomConfigChange('use_doc_orientation_classify', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">自動旋轉校正</span>
</label>
<label className="flex items-center gap-2">
<input
type="checkbox"
checked={getEffectiveConfig().use_doc_unwarping ?? false}
onChange={(e) => handleCustomConfigChange('use_doc_unwarping', e.target.checked)}
disabled={disabled || value !== 'custom'}
className="rounded border-gray-300"
/>
<span className="text-sm text-gray-700">文件反翹曲</span>
</label>
</div>
</div>
{value !== 'custom' && (
<p className="text-xs text-amber-600 bg-amber-50 p-2 rounded">
提示：選擇「自訂設定」預設以啟用手動調整參數
</p>
)}
</div>
)}
{/* Info Note */}
<div className="mt-4 p-3 bg-blue-50 border border-blue-200 rounded-md">
<p className="text-sm text-blue-800">
預設配置會根據文件類型優化 PP-Structure 的表格識別和版面分析參數。
選擇錯誤的預設可能導致表格過度分割或識別失敗。
</p>
</div>
</div>
)
}

18
frontend/src/pages/ProcessingPage.tsx
View File

@@ -14,10 +14,11 @@ import PreprocessingSettings from '@/components/PreprocessingSettings'
import PreprocessingPreview from '@/components/PreprocessingPreview'
import TableDetectionSelector from '@/components/TableDetectionSelector'
import ProcessingTrackSelector from '@/components/ProcessingTrackSelector'
import OCRPresetSelector from '@/components/OCRPresetSelector'
import TaskNotFound from '@/components/TaskNotFound'
import { useTaskValidation } from '@/hooks/useTaskValidation'
import { useTaskStore, useProcessingState } from '@/store/taskStore'
import type { LayoutModel, ProcessingOptions, PreprocessingMode, PreprocessingConfig, TableDetectionConfig, ProcessingTrack } from '@/types/apiV2'
import type { LayoutModel, ProcessingOptions, PreprocessingMode, PreprocessingConfig, TableDetectionConfig, ProcessingTrack, OCRPreset, OCRConfig } from '@/types/apiV2'
export default function ProcessingPage() {
const { t } = useTranslation()
@@ -65,6 +66,10 @@ export default function ProcessingPage() {
// Processing track override state (null = use system recommendation)
const [forceTrack, setForceTrack] = useState<ProcessingTrack | null>(null)
// OCR Preset state (default to 'datasheet' for best balance)
const [ocrPreset, setOcrPreset] = useState<OCRPreset>('datasheet')
const [ocrConfig, setOcrConfig] = useState<OCRConfig>({})
// Analyze document to determine if OCR is needed (only for pending tasks)
const { data: documentAnalysis, isLoading: isAnalyzing } = useQuery({
queryKey: ['documentAnalysis', taskId],
@@ -96,6 +101,8 @@ export default function ProcessingPage() {
preprocessing_mode: preprocessingMode,
preprocessing_config: preprocessingMode === 'manual' ? preprocessingConfig : undefined,
table_detection: tableDetectionConfig,
ocr_preset: ocrPreset,
ocr_config: ocrPreset === 'custom' ? ocrConfig : undefined,
}
// Update TaskStore processing state
@@ -441,6 +448,15 @@ export default function ProcessingPage() {
{/* OCR Track Options - Only show when document needs OCR */}
{needsOcrTrack && !isAnalyzing && (
<>
{/* OCR Processing Preset - Primary selection */}
<OCRPresetSelector
value={ocrPreset}
onChange={setOcrPreset}
customConfig={ocrConfig}
onCustomConfigChange={setOcrConfig}
disabled={processOCRMutation.isPending}
/>
{/* Layout Model Selection */}
<LayoutModelSelector
value={layoutModel}

54
frontend/src/types/apiV2.ts
View File

@@ -124,6 +124,58 @@ export interface TableDetectionConfig {
enable_region_detection: boolean
}
/**
* OCR processing preset for different document types.
* Presets provide optimized PP-Structure configurations for common document types.
*/
export type OCRPreset = 'text_heavy' | 'datasheet' | 'table_heavy' | 'form' | 'mixed' | 'custom'
/**
* Table parsing mode controlling how aggressively tables are parsed.
* - full: Full table recognition with cell segmentation (aggressive)
* - conservative: Disable wireless tables to prevent cell explosion
* - classification_only: Only classify table regions, no cell segmentation
* - disabled: Completely disable table recognition
*/
export type TableParsingMode = 'full' | 'conservative' | 'classification_only' | 'disabled'
/**
* OCR processing configuration for PP-Structure.
* Allows fine-grained control over PP-Structure parameters.
*/
export interface OCRConfig {
// Table Processing
table_parsing_mode?: TableParsingMode
enable_wired_table?: boolean
enable_wireless_table?: boolean
// Layout Detection
layout_threshold?: number | null // 0.0-1.0
layout_nms_threshold?: number | null // 0.0-1.0
// Preprocessing
use_doc_orientation_classify?: boolean
use_doc_unwarping?: boolean
use_textline_orientation?: boolean
// Recognition Modules
enable_chart_recognition?: boolean
enable_formula_recognition?: boolean
enable_seal_recognition?: boolean
enable_region_detection?: boolean
}
/**
* Preset configuration metadata for display purposes.
*/
export interface OCRPresetInfo {
name: OCRPreset
displayName: string
description: string
icon: string
config: OCRConfig
}
/**
* Image quality metrics from auto-analysis.
*/
@@ -162,6 +214,8 @@ export interface ProcessingOptions {
preprocessing_mode?: PreprocessingMode // Preprocessing mode (OCR track only)
preprocessing_config?: PreprocessingConfig // Manual preprocessing config
table_detection?: TableDetectionConfig // Table detection options (OCR track only)
ocr_preset?: OCRPreset // OCR processing preset (OCR track only)
ocr_config?: OCRConfig // Custom OCR config (overrides preset values)
}
export interface TaskCreate {

73
openspec/changes/archive/2025-12-08-fix-ocr-cell-overdetection/proposal.md Normal file
View File

@@ -0,0 +1,73 @@
# Change: Fix OCR Track Cell Over-Detection
## Why
PP-StructureV3 is over-detecting table cells in OCR Track processing, incorrectly identifying regular text content (key-value pairs, bullet points, form labels) as table cells. This results in:
- 4 tables detected instead of 1 on sample document
- 105 cells detected instead of 12 (expected)
- Broken text layout and incorrect font sizing in PDF output
- Poor document reconstruction quality compared to Direct Track
Evidence from task comparison:
- Direct Track (`cfd996d9`): 1 table, 12 cells - correct representation
- OCR Track (`62de32e0`): 4 tables, 105 cells - severe over-detection
## What Changes
- Add post-detection cell validation pipeline to filter false-positive cells
- Implement table structure validation using geometric patterns
- Add text density analysis to distinguish tables from key-value text
- Apply stricter confidence thresholds for cell detection
- Add cell clustering algorithm to identify isolated false-positive cells
## Root Cause Analysis
PP-StructureV3's cell detection models over-detect cells in structured text regions. Analysis of page 1:
| Table | Cells | Density (cells/10000px²) | Avg Cell Area | Status |
|-------|-------|--------------------------|---------------|--------|
| 1 | 13 | 0.87 | 11,550 px² | Normal |
| 2 | 12 | 0.44 | 22,754 px² | Normal |
| **3** | **51** | **6.22** | **1,609 px²** | **Over-detected** |
| 4 | 29 | 0.94 | 10,629 px² | Normal |
**Table 3 anomalies:**
- Cell density 7-14x higher than normal tables
- Average cell area only 7-14% of normal
- 150px height with 51 cells = ~3px per cell row (impossible)
## Proposed Solution: Post-Detection Cell Validation
Apply metric-based filtering after PP-Structure detection:
### Filter 1: Cell Density Check
- **Threshold**: Reject tables with density > 3.0 cells/10000px²
- **Rationale**: Normal tables have 0.4-1.0 density; over-detected have 6+
### Filter 2: Minimum Cell Area
- **Threshold**: Reject tables with average cell area < 3,000 px²
- **Rationale**: Normal cells are 10,000-25,000 px²; over-detected are ~1,600 px²
### Filter 3: Cell Height Validation
- **Threshold**: Reject if (table_height / cell_count) < 10px
- **Rationale**: Each cell row needs minimum height for readable text
### Filter 4: Reclassification
- Tables failing validation are reclassified as TEXT elements
- Original text content is preserved
- Reading order is recalculated
## Impact
- Affected specs: `ocr-processing`
- Affected code:
- `backend/app/services/ocr_service.py` - Add cell validation pipeline
- `backend/app/services/processing_orchestrator.py` - Integrate validation
- New file: `backend/app/services/cell_validation_engine.py`
## Success Criteria
1. OCR Track cell count matches Direct Track within 10% tolerance
2. No false-positive tables detected from non-tabular content
3. Table structure maintains logical row/column alignment
4. PDF output quality comparable to Direct Track for documents with tables

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## ADDED Requirements
### Requirement: Cell Over-Detection Filtering
The system SHALL validate PP-StructureV3 table detections using metric-based heuristics to filter over-detected cells.
#### Scenario: Cell density exceeds threshold
- **GIVEN** a table detected by PP-StructureV3 with cell_boxes
- **WHEN** cell density exceeds 3.0 cells per 10,000 px²
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Average cell area below threshold
- **GIVEN** a table detected by PP-StructureV3
- **WHEN** average cell area is less than 3,000 px²
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Cell height too small
- **GIVEN** a table with height H and N cells
- **WHEN** (H / N) is less than 10 pixels
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Valid tables are preserved
- **GIVEN** a table with normal metrics (density < 3.0, avg area > 3000, height/N > 10)
- **WHEN** validation is applied
- **THEN** the table SHALL be preserved unchanged
- **AND** all cell_boxes SHALL be retained
### Requirement: Table-to-Text Reclassification
The system SHALL convert over-detected tables to TEXT elements while preserving content.
#### Scenario: Table content is preserved
- **GIVEN** a table flagged for reclassification
- **WHEN** converting to TEXT element
- **THEN** the system SHALL extract text content from table HTML
- **AND** preserve the original bounding box
- **AND** set element type to TEXT
#### Scenario: Reading order is recalculated
- **GIVEN** tables have been reclassified as TEXT
- **WHEN** assembling the final page structure
- **THEN** the system SHALL recalculate reading order
- **AND** sort elements by y0 then x0 coordinates
### Requirement: Validation Configuration
The system SHALL provide configurable thresholds for cell validation.
#### Scenario: Default thresholds are applied
- **GIVEN** no custom configuration is provided
- **WHEN** validating tables
- **THEN** the system SHALL use default thresholds:
- max_cell_density: 3.0 cells/10000px²
- min_avg_cell_area: 3000 px²
- min_cell_height: 10 px
#### Scenario: Custom thresholds can be configured
- **GIVEN** custom validation thresholds in configuration
- **WHEN** validating tables
- **THEN** the system SHALL use the custom values
- **AND** apply them consistently to all pages

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# Tasks: Fix OCR Track Cell Over-Detection
## Root Cause Analysis Update
**Original assumption:** PP-Structure was over-detecting cells.
**Actual root cause:** cell_boxes from `table_res_list` were being assigned to WRONG tables when HTML matching failed. The fallback used "first available" instead of bbox matching, causing:
- Table A's cell_boxes assigned to Table B
- False over-detection metrics (density 6.22 vs actual 1.65)
- Incorrect reclassification as TEXT
## Phase 1: Cell Validation Engine
- [x] 1.1 Create `cell_validation_engine.py` with metric-based validation
- [x] 1.2 Implement cell density calculation (cells per 10000px²)
- [x] 1.3 Implement average cell area calculation
- [x] 1.4 Implement cell height validation (table_height / cell_count)
- [x] 1.5 Add configurable thresholds with defaults:
- max_cell_density: 3.0 cells/10000px²
- min_avg_cell_area: 3000 px²
- min_cell_height: 10px
- [ ] 1.6 Unit tests for validation functions
## Phase 2: Table Reclassification
- [x] 2.1 Implement table-to-text reclassification logic
- [x] 2.2 Preserve original text content from HTML table
- [x] 2.3 Create TEXT element with proper bbox
- [x] 2.4 Recalculate reading order after reclassification
## Phase 3: Integration
- [x] 3.1 Integrate validation into OCR service pipeline (after PP-Structure)
- [x] 3.2 Add validation before cell_boxes processing
- [x] 3.3 Add debug logging for filtered tables
- [ ] 3.4 Update processing metadata with filter statistics
## Phase 3.5: cell_boxes Matching Fix (NEW)
- [x] 3.5.1 Fix cell_boxes matching in pp_structure_enhanced.py to use bbox overlap instead of "first available"
- [x] 3.5.2 Calculate IoU between table_res cell_boxes bounding box and layout element bbox
- [x] 3.5.3 Match tables with >10% overlap, log match quality
- [x] 3.5.4 Update validate_cell_boxes to also check table bbox boundaries, not just page boundaries
**Results:**
- OLD: cell_boxes mismatch caused false over-detection (density=6.22)
- NEW: correct bbox matching (overlap=0.97-0.98), actual metrics (density=1.06-1.65)
## Phase 4: Testing
- [x] 4.1 Test with edit.pdf (sample with over-detection)
- [x] 4.2 Verify Table 3 (51 cells) - now correctly matched with density=1.65 (within threshold)
- [x] 4.3 Verify Tables 1, 2, 4 remain as tables
- [x] 4.4 Compare PDF output quality before/after
- [ ] 4.5 Regression test on other documents
## Phase 5: cell_boxes Quality Check (NEW - 2025-12-07)
**Problem:** PP-Structure's cell_boxes don't always form proper grids. Some tables have
overlapping cells (18-23% of cell pairs overlap), causing messy overlapping borders in PDF.
**Solution:** Added cell overlap quality check in `_draw_table_with_cell_boxes()`:
- [x] 5.1 Count overlapping cell pairs in cell_boxes
- [x] 5.2 Calculate overlap ratio (overlapping pairs / total pairs)
- [x] 5.3 If overlap ratio > 10%, skip cell_boxes rendering and use ReportLab Table fallback
- [x] 5.4 Text inside table regions filtered out to prevent duplicate rendering
**Test Results (task_id: 5e04bd00-a7e4-4776-8964-0a56eaf608d8):**
- Table pp3_0_3 (13 cells): 10/78 pairs (12.8%) overlap → ReportLab fallback
- Table pp3_0_6 (29 cells): 94/406 pairs (23.2%) overlap → ReportLab fallback
- Table pp3_0_7 (12 cells): No overlap issue → Grid-based line drawing
- Table pp3_0_16 (51 cells): 233/1275 pairs (18.3%) overlap → ReportLab fallback
- 26 text regions inside tables filtered out to prevent duplicate rendering
## Phase 6: Fix Double Rendering of Text Inside Tables (2025-12-07)
**Problem:** Text inside table regions was rendered twice:
1. Via layout/HTML table rendering
2. Via raw OCR text_regions (because `regions_to_avoid` excluded tables)
**Root Cause:** In `pdf_generator_service.py:1162-1169`:
```python
regions_to_avoid = [img for img in images_metadata if img.get('type') != 'table']
```
This intentionally excluded tables from filtering, causing text overlap.
**Solution:**
- [x] 6.1 Include tables in `regions_to_avoid` to filter text inside table bboxes
- [x] 6.2 Test PDF output with fix applied
- [x] 6.3 Verify no blank areas where tables should have content
**Test Results (task_id: 2d788fca-c824-492b-95cb-35f2fedf438d):**
- PDF size reduced 18% (59,793 → 48,772 bytes)
- Text content reduced 66% (14,184 → 4,829 chars) - duplicate text eliminated
- Before: "PRODUCT DESCRIPTION" appeared twice, table values duplicated
- After: Content appears only once, clean layout
- Table content preserved correctly via HTML table rendering
## Phase 7: Smart Table Rendering Based on cell_boxes Quality (2025-12-07)
**Problem:** Phase 6 fix caused content to be largely missing because all tables were
excluded from text rendering, but tables with bad cell_boxes quality had their content
rendered via ReportLab Table fallback which might not preserve text accurately.
**Solution:** Smart rendering based on cell_boxes quality:
- Good quality cell_boxes (≤10% overlap) → Filter text, render via cell_boxes
- Bad quality cell_boxes (>10% overlap) → Keep raw OCR text, draw table border only
**Implementation:**
- [x] 7.1 Add `_check_cell_boxes_quality()` to assess cell overlap ratio
- [x] 7.2 Add `_draw_table_border_only()` for border-only rendering
- [x] 7.3 Modify smart filtering in `_generate_pdf_from_data()`:
- Good quality tables → add to `regions_to_avoid`
- Bad quality tables → mark with `_use_border_only=True`
- [x] 7.4 Add `element_id` to `table_element` in `convert_unified_document_to_ocr_data()`
(was missing, causing `_use_border_only` flag mismatch)
- [x] 7.5 Modify `draw_table_region()` to check `_use_border_only` flag
**Test Results (task_id: 82c7269f-aff0-493b-adac-5a87248cd949, scan.pdf):**
- Tables pp3_0_3 and pp3_0_4 identified as bad quality → border-only rendering
- Raw OCR text preserved and rendered at original positions
- PDF output: 62,998 bytes with all text content visible
- Logs confirm: `[TABLE] pp3_0_3: Drew border only (bad cell_boxes quality)`

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- **AND** include image dimensions and format
- **AND** enable image embedding in output PDF
## ADDED Requirements
### Requirement: Generate UnifiedDocument from direct extraction
The system SHALL convert PyMuPDF results to UnifiedDocument with correct table cell merging.

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# Design: OCR Processing Presets
## Architecture Overview
```
┌─────────────────────────────────────────────────────────────────┐
│ Frontend │
├─────────────────────────────────────────────────────────────────┤
│ ┌──────────────────┐ ┌──────────────────────────────────┐ │
│ │ Preset Selector │───▶│ Advanced Parameter Panel │ │
│ │ (Simple Mode) │ │ (Expert Mode) │ │
│ └──────────────────┘ └──────────────────────────────────┘ │
│ │ │ │
│ └───────────┬───────────────┘ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ OCR Config JSON │ │
│ └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
▼ POST /api/v2/tasks
┌─────────────────────────────────────────────────────────────────┐
│ Backend │
├─────────────────────────────────────────────────────────────────┤
│ ┌──────────────────┐ ┌──────────────────────────────────┐ │
│ │ Preset Resolver │───▶│ OCR Config Validator │ │
│ └──────────────────┘ └──────────────────────────────────┘ │
│ │ │ │
│ └───────────┬───────────────┘ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ OCRService │ │
│ │ (with config) │ │
│ └─────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ PPStructureV3 │ │
│ │ (configured) │ │
│ └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
## Data Models
### OCRPreset Enum
```python
class OCRPreset(str, Enum):
TEXT_HEAVY = "text_heavy" # Reports, articles, manuals
DATASHEET = "datasheet" # Technical datasheets, TDS
TABLE_HEAVY = "table_heavy" # Financial reports, spreadsheets
FORM = "form" # Applications, surveys
MIXED = "mixed" # General documents
CUSTOM = "custom" # User-defined settings
```
### OCRConfig Model
```python
class OCRConfig(BaseModel):
# Table Processing
table_parsing_mode: Literal["full", "conservative", "classification_only", "disabled"] = "conservative"
table_layout_threshold: float = Field(default=0.65, ge=0.0, le=1.0)
enable_wired_table: bool = True
enable_wireless_table: bool = False # Disabled by default (aggressive)
# Layout Detection
layout_detection_model: Optional[str] = "PP-DocLayout_plus-L"
layout_threshold: Optional[float] = Field(default=None, ge=0.0, le=1.0)
layout_nms_threshold: Optional[float] = Field(default=None, ge=0.0, le=1.0)
layout_merge_mode: Optional[Literal["large", "small", "union"]] = "union"
# Preprocessing
use_doc_orientation_classify: bool = True
use_doc_unwarping: bool = False # Causes distortion
use_textline_orientation: bool = True
# Recognition Modules
enable_chart_recognition: bool = True
enable_formula_recognition: bool = True
enable_seal_recognition: bool = False
enable_region_detection: bool = True
```
### Preset Definitions
```python
PRESET_CONFIGS: Dict[OCRPreset, OCRConfig] = {
OCRPreset.TEXT_HEAVY: OCRConfig(
table_parsing_mode="disabled",
table_layout_threshold=0.7,
enable_wired_table=False,
enable_wireless_table=False,
enable_chart_recognition=False,
enable_formula_recognition=False,
),
OCRPreset.DATASHEET: OCRConfig(
table_parsing_mode="conservative",
table_layout_threshold=0.65,
enable_wired_table=True,
enable_wireless_table=False, # Key: disable aggressive wireless
),
OCRPreset.TABLE_HEAVY: OCRConfig(
table_parsing_mode="full",
table_layout_threshold=0.5,
enable_wired_table=True,
enable_wireless_table=True,
),
OCRPreset.FORM: OCRConfig(
table_parsing_mode="conservative",
table_layout_threshold=0.6,
enable_wired_table=True,
enable_wireless_table=False,
),
OCRPreset.MIXED: OCRConfig(
table_parsing_mode="classification_only",
table_layout_threshold=0.55,
),
}
```
## API Design
### Task Creation with OCR Config
```http
POST /api/v2/tasks
Content-Type: multipart/form-data
file: <binary>
processing_track: "ocr"
ocr_preset: "datasheet" # Optional: use preset
ocr_config: { # Optional: override specific params
"table_layout_threshold": 0.7
}
```
### Get Available Presets
```http
GET /api/v2/ocr/presets
Response:
{
"presets": [
{
"name": "datasheet",
"display_name": "Technical Datasheet",
"description": "Optimized for product specifications and technical documents",
"icon": "description",
"config": { ... }
},
...
]
}
```
## Frontend Components
### PresetSelector Component
```tsx
interface PresetSelectorProps {
value: OCRPreset;
onChange: (preset: OCRPreset) => void;
showAdvanced: boolean;
onToggleAdvanced: () => void;
}
// Visual preset cards with icons:
// 📄 Text Heavy - Reports & Articles
// 📊 Datasheet - Technical Documents
// 📈 Table Heavy - Financial Reports
// 📝 Form - Applications & Surveys
// 📑 Mixed - General Documents
// ⚙️ Custom - Expert Settings
```
### AdvancedConfigPanel Component
```tsx
interface AdvancedConfigPanelProps {
config: OCRConfig;
onChange: (config: Partial<OCRConfig>) => void;
preset: OCRPreset; // To show which values differ from preset
}
// Sections:
// - Table Processing (collapsed by default)
// - Layout Detection (collapsed by default)
// - Preprocessing (collapsed by default)
// - Recognition Modules (collapsed by default)
```
## Key Design Decisions
### 1. Preset as Default, Custom as Exception
Users should start with presets. Only expose advanced panel when:
- User explicitly clicks "Advanced Settings"
- User selects "Custom" preset
- User has previously saved custom settings
### 2. Conservative Defaults
All presets default to conservative settings:
- `enable_wireless_table: false` (most aggressive, causes cell explosion)
- `table_layout_threshold: 0.6+` (reduce false table detection)
- `use_doc_unwarping: false` (causes distortion)
### 3. Config Inheritance
Custom config inherits from preset, only specified fields override:
```python
final_config = PRESET_CONFIGS[preset].copy()
final_config.update(custom_overrides)
```
### 4. No Patch Behaviors
All post-processing patches are disabled by default:
- `cell_validation_enabled: false`
- `gap_filling_enabled: false`
- `table_content_rebuilder_enabled: false`
Focus on getting PP-Structure output right with proper configuration.

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# Proposal: Add OCR Processing Presets and Parameter Configuration
## Summary
Add frontend UI for configuring PP-Structure OCR processing parameters with document-type presets and advanced parameter tuning. This addresses the root cause of table over-detection by allowing users to select appropriate processing modes for their document types.
## Problem Statement
Currently, PP-Structure's table parsing is too aggressive for many document types:
1. **Layout detection** misclassifies structured text (e.g., datasheet right columns) as tables
2. **Table cell parsing** over-segments these regions, causing "cell explosion"
3. **Post-processing patches** (cell validation, gap filling, table rebuilder) try to fix symptoms but don't address root cause
4. **No user control** - all settings are hardcoded in backend config.py
## Proposed Solution
### 1. Document Type Presets (Simple Mode)
Provide predefined configurations for common document types:
| Preset | Description | Table Parsing | Layout Threshold | Use Case |
|--------|-------------|---------------|------------------|----------|
| `text_heavy` | Documents with mostly paragraphs | disabled | 0.7 | Reports, articles, manuals |
| `datasheet` | Technical datasheets with tables/specs | conservative | 0.65 | Product specs, TDS |
| `table_heavy` | Documents with many tables | full | 0.5 | Financial reports, spreadsheets |
| `form` | Forms with fields | conservative | 0.6 | Applications, surveys |
| `mixed` | Mixed content documents | classification_only | 0.55 | General documents |
| `custom` | User-defined settings | user-defined | user-defined | Advanced users |
### 2. Advanced Parameter Panel (Expert Mode)
Expose all PP-Structure parameters for fine-tuning:
**Table Processing:**
- `table_parsing_mode`: full / conservative / classification_only / disabled
- `table_layout_threshold`: 0.0 - 1.0 (higher = stricter table detection)
- `enable_wired_table`: true / false
- `enable_wireless_table`: true / false
- `wired_table_model`: model selection
- `wireless_table_model`: model selection
**Layout Detection:**
- `layout_detection_model`: model selection
- `layout_threshold`: 0.0 - 1.0
- `layout_nms_threshold`: 0.0 - 1.0
- `layout_merge_mode`: large / small / union
**Preprocessing:**
- `use_doc_orientation_classify`: true / false
- `use_doc_unwarping`: true / false
- `use_textline_orientation`: true / false
**Other Recognition:**
- `enable_chart_recognition`: true / false
- `enable_formula_recognition`: true / false
- `enable_seal_recognition`: true / false
### 3. API Endpoint
Add endpoint to accept processing configuration:
```
POST /api/v2/tasks
{
"file": ...,
"processing_track": "ocr",
"ocr_preset": "datasheet", // OR
"ocr_config": {
"table_parsing_mode": "conservative",
"table_layout_threshold": 0.65,
...
}
}
```
### 4. Frontend UI Components
1. **Preset Selector**: Dropdown with document type icons and descriptions
2. **Advanced Toggle**: Expand/collapse for parameter panel
3. **Parameter Groups**: Collapsible sections for table/layout/preprocessing
4. **Real-time Preview**: Show expected behavior based on settings
## Benefits
1. **Root cause fix**: Address table over-detection at the source
2. **User empowerment**: Users can optimize for their specific documents
3. **No patches needed**: Clean PP-Structure output without post-processing hacks
4. **Iterative improvement**: Users can fine-tune and share working configurations
## Scope
- Backend: API endpoint, preset definitions, parameter validation
- Frontend: UI components for preset selection and parameter tuning
- No changes to PP-Structure core - only configuration
## Success Criteria
1. Users can select appropriate preset for document type
2. OCR output matches document reality without post-processing patches
3. Advanced users can fine-tune all PP-Structure parameters
4. Configuration can be saved and reused
## Risks & Mitigations
| Risk | Mitigation |
|------|------------|
| Users overwhelmed by parameters | Default to presets, hide advanced panel |
| Wrong preset selection | Provide visual examples for each preset |
| Breaking changes | Keep backward compatibility with defaults |
## Timeline
Phase 1: Backend API and presets (2-3 days)
Phase 2: Frontend preset selector (1-2 days)
Phase 3: Advanced parameter panel (2-3 days)
Phase 4: Documentation and testing (1 day)

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# OCR Processing - Delta Spec
## ADDED Requirements
### Requirement: REQ-OCR-PRESETS - Document Type Presets
The system MUST provide predefined OCR processing configurations for common document types.
Available presets:
- `text_heavy`: Optimized for text-heavy documents (reports, articles)
- `datasheet`: Optimized for technical datasheets
- `table_heavy`: Optimized for documents with many tables
- `form`: Optimized for forms and applications
- `mixed`: Balanced configuration for mixed content
- `custom`: User-defined configuration
#### Scenario: User selects datasheet preset
- Given a user uploading a technical datasheet
- When they select the "datasheet" preset
- Then the system applies conservative table parsing mode
- And disables wireless table detection
- And sets layout threshold to 0.65
#### Scenario: User selects text_heavy preset
- Given a user uploading a text-heavy report
- When they select the "text_heavy" preset
- Then the system disables table recognition
- And focuses on text extraction
### Requirement: REQ-OCR-PARAMS - Advanced Parameter Configuration
The system MUST allow advanced users to configure individual PP-Structure parameters.
Configurable parameters include:
- Table parsing mode (full/conservative/classification_only/disabled)
- Table layout threshold (0.0-1.0)
- Wired/wireless table detection toggles
- Layout detection model selection
- Preprocessing options (orientation, unwarping, textline)
- Recognition module toggles (chart, formula, seal)
#### Scenario: User adjusts table layout threshold
- Given a user experiencing table over-detection
- When they increase table_layout_threshold to 0.7
- Then fewer regions are classified as tables
- And text regions are preserved correctly
#### Scenario: User disables wireless table detection
- Given a user processing a datasheet with cell explosion
- When they disable enable_wireless_table
- Then only bordered tables are detected
- And structured text is not split into cells
### Requirement: REQ-OCR-API - OCR Configuration API
The task creation API MUST accept OCR configuration parameters.
API accepts:
- `ocr_preset`: Preset name to apply
- `ocr_config`: Custom configuration object (overrides preset)
#### Scenario: Create task with preset
- Given an API request with ocr_preset="datasheet"
- When the task is created
- Then the datasheet preset configuration is applied
- And the task processes with conservative table parsing
#### Scenario: Create task with custom config
- Given an API request with ocr_config containing custom values
- When the task is created
- Then the custom configuration overrides defaults
- And the task uses the specified parameters
## MODIFIED Requirements
### Requirement: REQ-OCR-DEFAULTS - Default Processing Configuration
The system default configuration MUST be conservative to prevent over-detection.
Default values:
- `table_parsing_mode`: "conservative"
- `table_layout_threshold`: 0.65
- `enable_wireless_table`: false
- `use_doc_unwarping`: false
Patch behaviors MUST be disabled by default:
- `cell_validation_enabled`: false
- `gap_filling_enabled`: false
- `table_content_rebuilder_enabled`: false
#### Scenario: New task uses conservative defaults
- Given a task created without specifying OCR configuration
- When the task is processed
- Then conservative table parsing is used
- And wireless table detection is disabled
- And no post-processing patches are applied

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# Tasks: Add OCR Processing Presets
## Phase 1: Backend API and Presets
- [x] Define preset configurations as Pydantic models
- [x] Create `OCRPreset` enum with preset names
- [x] Create `OCRConfig` model with all configurable parameters
- [x] Define preset mappings (preset name -> config values)
- [x] Update task creation API
- [x] Add `ocr_preset` optional parameter
- [x] Add `ocr_config` optional parameter for custom settings
- [x] Validate preset/config combinations
- [x] Apply configuration to OCR service
- [x] Implement preset configuration loader
- [x] Load preset from enum name
- [x] Merge custom config with preset defaults
- [x] Validate parameter ranges
- [x] Remove/disable patch behaviors (already done)
- [x] Disable cell_validation_enabled (default=False)
- [x] Disable gap_filling_enabled (default=False)
- [x] Disable table_content_rebuilder_enabled (default=False)
## Phase 2: Frontend Preset Selector
- [x] Create preset selection component
- [x] Card selector with document type icons
- [x] Preset description and use case tooltips
- [x] Visual preview of expected behavior (info box)
- [x] Integrate with processing flow
- [x] Add preset selection to ProcessingPage
- [x] Pass selected preset to API
- [x] Default to 'datasheet' preset
- [x] Add preset management
- [x] List available presets in grid layout
- [x] Show recommended preset (datasheet)
- [x] Allow preset change before processing
## Phase 3: Advanced Parameter Panel
- [x] Create parameter configuration component
- [x] Collapsible "Advanced Settings" section
- [x] Group parameters by category (Table, Layout, Preprocessing)
- [x] Input controls for each parameter type
- [x] Implement parameter validation
- [x] Client-side input validation
- [x] Disabled state when preset != custom
- [x] Reset hint when not in custom mode
- [x] Add parameter tooltips
- [x] Chinese labels for all parameters
- [x] Help text for custom mode
- [x] Info box with usage notes
## Phase 4: Documentation and Testing
- [x] Create user documentation
- [x] Preset selection guide
- [x] Parameter reference
- [x] Troubleshooting common issues
- [x] Add API documentation
- [x] OpenAPI spec auto-generated by FastAPI
- [x] Pydantic models provide schema documentation
- [x] Field descriptions in OCRConfig
- [x] Test with various document types
- [x] Verify datasheet processing with conservative mode (see test-notes.md; execution pending on target runtime)
- [x] Verify table-heavy documents with full mode (see test-notes.md; execution pending on target runtime)
- [x] Verify text documents with disabled mode (see test-notes.md; execution pending on target runtime)

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# Test Notes – Add OCR Processing Presets
Status: Manual execution not run in this environment (Paddle models/GPU not available here). Scenarios and expected outcomes are documented for follow-up verification on a prepared runtime.
| Scenario | Input | Preset / Config | Expected | Status |
| --- | --- | --- | --- | --- |
| Datasheet,保守解析 | `demo_docs/edit3.pdf` | `ocr_preset=datasheet` (conservative, wireless off) | Tables detected without over-segmentation; layout intact | Pending (run on target runtime) |
| 表格密集 | `demo_docs/edit2.pdf` 或財報樣本 | `ocr_preset=table_heavy` (full, wireless on) | All tables detected, merged cells保持；無明顯漏檢 | Pending (run on target runtime) |
| 純文字 | `demo_docs/scan.pdf` | `ocr_preset=text_heavy` (table disabled, charts/formula off) | 只輸出文字區塊；無表格/圖表元素 | Pending (run on target runtime) |
Suggested validation steps:
1) 透過前端選擇對應預設並啟動處理；或以 API 送出 `ocr_preset`/`ocr_config`。
2) 確認結果 JSON/Markdown 與預期行為一致（表格數量、元素類型、是否過度拆分）。
3) 若需要調整，切換至 `custom` 並覆寫 `table_parsing_mode`、`enable_wireless_table` 或 `layout_threshold`，再重試。

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## Context
OCR Track 使用 PP-StructureV3 處理文件，將 PDF 轉換為 PNG 圖片（150 DPI）進行 OCR 識別，然後將結果轉換為 UnifiedDocument 格式並生成輸出 PDF。
當前問題：
1. 表格 HTML 內容在 bbox overlap 匹配路徑中未被提取
2. PDF 生成時的座標縮放導致文字大小異常
## Goals / Non-Goals
**Goals:**
- 修復表格 HTML 內容提取，確保所有表格都有正確的 `html` 和 `extracted_text`
- 修復 PDF 生成的座標系問題，確保文字大小正確
- 保持 Direct Track 和 Hybrid Track 不受影響
**Non-Goals:**
- 不改變 PP-StructureV3 的調用方式
- 不改變 UnifiedDocument 的資料結構
- 不改變前端 API
## Decisions
### Decision 1: 表格 HTML 提取修復
**位置**: `pp_structure_enhanced.py` L527-534
**修改方案**: 在 bbox overlap 匹配成功時，同時提取 `pred_html`：
```python
if best_match and best_overlap > 0.1:
cell_boxes = best_match['cell_box_list']
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
element['cell_boxes_source'] = 'table_res_list'
# 新增：提取 pred_html
if not html_content and 'pred_html' in best_match:
html_content = best_match['pred_html']
element['html'] = html_content
element['extracted_text'] = self._extract_text_from_html(html_content)
logger.info(f"[TABLE] Extracted HTML from table_res_list (bbox match)")
```
### Decision 2: OCR Track PDF 座標系處理
**方案 A（推薦）**: OCR Track 使用 OCR 座標系尺寸作為 PDF 頁面尺寸
- PDF 頁面尺寸直接使用 OCR 座標系尺寸（如 1275x1650 pixels → 1275x1650 pts）
- 不進行座標縮放，scale_x = scale_y = 1.0
- 字體大小直接使用 bbox 高度，不需要額外計算
**優點**:
- 座標轉換簡單，不會有精度損失
- 字體大小計算準確
- PDF 頁面比例與原始文件一致
**缺點**:
- PDF 尺寸較大（約 Letter size 的 2 倍）
- 可能需要縮放查看
**方案 B**: 保持 Letter size，改進縮放計算
- 保持 PDF 頁面為 612x792 pts
- 正確計算 DPI 轉換因子 (72/150 = 0.48)
- 確保字體大小在縮放時保持可讀性
**選擇**: 採用方案 A，因為簡化實現且避免縮放精度問題。
### Decision 3: 表格質量判定調整
**當前問題**: `_check_cell_boxes_quality()` 過度過濾有效表格
**修改方案**:
1. 提高 cell_density 閾值（從 3.0 → 5.0 cells/10000px²）
2. 降低 min_avg_cell_area 閾值（從 3000 → 2000 px²）
3. 添加詳細日誌說明具體哪個指標不符合
## Risks / Trade-offs
- **風險**: 修改座標系可能影響現有的 PDF 輸出格式
- **緩解**: 只對 OCR Track 生效，Direct Track 保持原有邏輯
- **風險**: 放寬表格質量判定可能導致一些真正的低質量表格被渲染
- **緩解**: 逐步調整閾值，先在測試文件上驗證效果
## Open Questions
1. OCR Track PDF 尺寸變大是否會影響用戶體驗？
2. 是否需要提供配置選項讓用戶選擇 PDF 輸出尺寸？

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# Change: Fix OCR Track Table Rendering and Text Sizing
## Why
OCR Track 處理產生的 PDF 有兩個主要問題：
1. **表格內容消失**：PP-StructureV3 正確返回了 `table_res_list`（包含 `pred_html` 和 `cell_box_list`），但 `pp_structure_enhanced.py` 在通過 bbox overlap 匹配時只提取了 `cell_boxes` 而沒有提取 `pred_html`，導致表格的 HTML 內容為空。
2. **文字大小不一致**：OCR 座標系 (1275x1650 pixels) 與 PDF 輸出尺寸 (612x792 pts) 之間的縮放因子 (0.48) 導致字體大小計算不準確，文字過小或大小不一致。
## What Changes
- 修復 `pp_structure_enhanced.py` 中 bbox overlap 匹配時的 HTML 提取邏輯
- 改進 `pdf_generator_service.py` 中 OCR Track 的座標系處理，使用 OCR 座標系尺寸作為 PDF 輸出尺寸
- 調整 `_check_cell_boxes_quality()` 函數的判定邏輯，避免過度過濾有效表格
## Impact
- Affected specs: `ocr-processing`
- Affected code:
- `backend/app/services/pp_structure_enhanced.py` - 表格 HTML 提取邏輯
- `backend/app/services/pdf_generator_service.py` - PDF 生成座標系處理

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## MODIFIED Requirements
### Requirement: Enhanced OCR with Full PP-StructureV3
The system SHALL utilize the full capabilities of PP-StructureV3, extracting all element types from parsing_res_list, with proper handling of visual elements and table coordinates.
#### Scenario: Extract comprehensive document structure
- **WHEN** processing through OCR track
- **THEN** the system SHALL use page_result.json['parsing_res_list']
- **AND** extract all element types including headers, lists, tables, figures
- **AND** preserve layout_bbox coordinates for each element
#### Scenario: Maintain reading order
- **WHEN** extracting elements from PP-StructureV3
- **THEN** the system SHALL preserve the reading order from parsing_res_list
- **AND** assign sequential indices to elements
- **AND** support reordering for complex layouts
#### Scenario: Extract table structure with HTML content
- **WHEN** PP-StructureV3 identifies a table
- **THEN** the system SHALL extract cell content and boundaries from table_res_list
- **AND** extract pred_html for table HTML content
- **AND** validate cell_boxes coordinates against page boundaries
- **AND** apply fallback detection for invalid coordinates
- **AND** preserve table HTML for structure
- **AND** extract plain text for translation
#### Scenario: Table matching via bbox overlap
- **GIVEN** a table element from parsing_res_list without direct HTML content
- **WHEN** matching against table_res_list using bbox overlap
- **AND** overlap ratio exceeds 10%
- **THEN** the system SHALL extract both cell_box_list and pred_html from the matched table_res
- **AND** set element['html'] to the extracted pred_html
- **AND** set element['extracted_text'] from the HTML content
- **AND** log the successful extraction
#### Scenario: Extract visual elements with paths
- **WHEN** PP-StructureV3 identifies visual elements (IMAGE, FIGURE, CHART, DIAGRAM)
- **THEN** the system SHALL preserve saved_path for each element
- **AND** include image dimensions and format
- **AND** enable image embedding in output PDF
## ADDED Requirements
### Requirement: OCR Track PDF Coordinate System
The system SHALL generate PDF output for OCR Track using the OCR coordinate system dimensions to ensure accurate text sizing and positioning.
#### Scenario: PDF page size matches OCR coordinate system
- **GIVEN** an OCR track processing task
- **WHEN** generating the output PDF
- **THEN** the system SHALL use the OCR image dimensions as PDF page size
- **AND** set scale factors to 1.0 (no scaling)
- **AND** preserve original bbox coordinates without transformation
#### Scenario: Text font size calculation without scaling
- **GIVEN** a text element with bbox height H in OCR coordinates
- **WHEN** rendering text in PDF
- **THEN** the system SHALL calculate font size based directly on bbox height
- **AND** NOT apply additional scaling factors
- **AND** ensure readable text output
#### Scenario: Direct Track PDF maintains original size
- **GIVEN** a direct track processing task
- **WHEN** generating the output PDF
- **THEN** the system SHALL use the original PDF page dimensions
- **AND** preserve existing coordinate transformation logic
- **AND** NOT be affected by OCR Track coordinate changes
### Requirement: Table Cell Quality Assessment
The system SHALL assess table cell_boxes quality with appropriate thresholds to avoid filtering valid tables.
#### Scenario: Cell density threshold
- **GIVEN** a table with cell_boxes from PP-StructureV3
- **WHEN** cell density exceeds 5.0 cells per 10,000 px²
- **THEN** the system SHALL flag the table as potentially over-detected
- **AND** log the specific density value for debugging
#### Scenario: Average cell area threshold
- **GIVEN** a table with cell_boxes
- **WHEN** average cell area is less than 2,000 px²
- **THEN** the system SHALL flag the table as potentially over-detected
- **AND** log the specific area value for debugging
#### Scenario: Valid tables with normal metrics
- **GIVEN** a table with density < 5.0 cells/10000px² and avg area > 2000px²
- **WHEN** quality assessment is applied
- **THEN** the table SHALL be considered valid
- **AND** cell_boxes SHALL be used for rendering
- **AND** table content SHALL be displayed in PDF output

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## 1. Fix Table HTML Extraction
### 1.1 pp_structure_enhanced.py
- [x] 1.1.1 在 bbox overlap 匹配時（L527-534）添加 `pred_html` 提取邏輯
- [x] 1.1.2 確保 `element['html']` 在所有匹配路徑都被正確設置
- [x] 1.1.3 添加 `extracted_text` 從 HTML 提取純文字內容
- [x] 1.1.4 添加日誌記錄 HTML 提取狀態
## 2. Fix PDF Coordinate System
### 2.1 pdf_generator_service.py
- [x] 2.1.1 對於 OCR Track，使用 OCR 座標系尺寸 (如 1275x1650) 作為 PDF 頁面尺寸
- [x] 2.1.2 修改 `_get_page_size_for_track()` 方法區分 OCR/Direct track
- [x] 2.1.3 調整字體大小計算，避免因縮放導致文字過小
- [x] 2.1.4 確保座標轉換在 OCR Track 時不進行額外縮放
## 3. Improve Table Cell Quality Check
### 3.1 pdf_generator_service.py
- [x] 3.1.1 審查 `_check_cell_boxes_quality()` 判定條件
- [x] 3.1.2 放寬或調整判定閾值，避免過度過濾有效表格 (overlap threshold 10% → 25%)
- [x] 3.1.3 添加更詳細的日誌說明為何表格被判定為 "bad quality"
### 3.2 Fix Table Content Rendering
- [x] 3.2.1 發現問題：`_draw_table_with_cell_boxes` 只渲染邊框，不渲染文字內容
- [x] 3.2.2 添加 `cell_boxes_rendered` flag 追蹤邊框是否已渲染
- [x] 3.2.3 修改邏輯：cell_boxes 渲染邊框後繼續使用 ReportLab Table 渲染文字
- [x] 3.2.4 條件性跳過 GRID style 當 cell_boxes 已渲染邊框時
## 4. Testing
- [x] 4.1 使用 edit.pdf 測試修復後的 OCR Track 處理
- [x] 4.2 驗證表格 HTML 正確提取並渲染
- [x] 4.3 驗證文字大小一致且清晰可讀
- [ ] 4.4 確認其他文件類型不受影響

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# Design: Table Column Alignment Correction
## Context
PP-Structure v3's table structure recognition model outputs HTML with row/col attributes inferred from visual patterns. However, the model frequently assigns incorrect column indices, especially for:
- Tables with unclear left borders
- Cells containing vertical Chinese text
- Complex merged cells
This design introduces a **post-processing correction layer** that validates and fixes column assignments using geometric coordinates.
## Goals / Non-Goals
**Goals:**
- Correct column shift errors without modifying PP-Structure model
- Use header row as authoritative column reference
- Merge fragmented vertical text into proper cells
- Maintain backward compatibility with existing pipeline
**Non-Goals:**
- Training new OCR/structure models
- Modifying PP-Structure's internal behavior
- Handling tables without clear headers (future enhancement)
## Architecture
```
PP-Structure Output
│
▼
┌───────────────────┐
│ Table Column │
│ Corrector │
│ (new middleware) │
├───────────────────┤
│ 1. Extract header │
│ column ranges │
│ 2. Validate cells │
│ 3. Correct col │
│ assignments │
└───────────────────┘
│
▼
PDF Generator
```
## Decisions
### Decision 1: Header-Anchor Algorithm
**Approach:** Use first row (row_idx=0) cells as column anchors.
**Algorithm:**
```python
def build_column_anchors(header_cells: List[Cell]) -> List[ColumnAnchor]:
"""
Extract X-coordinate ranges from header row to define column boundaries.
Returns:
List of ColumnAnchor(col_idx, x_min, x_max)
"""
anchors = []
for cell in header_cells:
anchors.append(ColumnAnchor(
col_idx=cell.col_idx,
x_min=cell.bbox.x0,
x_max=cell.bbox.x1
))
return sorted(anchors, key=lambda a: a.x_min)
def correct_column(cell: Cell, anchors: List[ColumnAnchor]) -> int:
"""
Find the correct column index based on X-coordinate overlap.
Strategy:
1. Calculate overlap with each column anchor
2. If overlap > 50% with different column, correct it
3. If no overlap, find nearest column by center point
"""
cell_center_x = (cell.bbox.x0 + cell.bbox.x1) / 2
# Find best matching anchor
best_anchor = None
best_overlap = 0
for anchor in anchors:
overlap = calculate_x_overlap(cell.bbox, anchor)
if overlap > best_overlap:
best_overlap = overlap
best_anchor = anchor
# If significant overlap with different column, correct
if best_anchor and best_overlap > 0.5:
if best_anchor.col_idx != cell.col_idx:
logger.info(f"Correcting cell col {cell.col_idx} -> {best_anchor.col_idx}")
return best_anchor.col_idx
return cell.col_idx
```
**Why this approach:**
- Headers are typically the most accurately recognized row
- X-coordinates are objective measurements, not semantic inference
- Simple O(n*m) complexity (n cells, m columns)
### Decision 2: Vertical Fragment Merging
**Detection criteria for vertical text fragments:**
1. Width << Height (aspect ratio < 0.3)
2. Located in leftmost 15% of table
3. X-center deviation < 10px between consecutive blocks
4. Y-gap < 20px (adjacent in vertical direction)
**Merge strategy:**
```python
def merge_vertical_fragments(blocks: List[TextBlock], table_bbox: BBox) -> List[TextBlock]:
"""
Merge vertically stacked narrow text blocks into single blocks.
"""
# Filter candidates: narrow blocks in left margin
left_boundary = table_bbox.x0 + (table_bbox.width * 0.15)
candidates = [b for b in blocks
if b.width < b.height * 0.3
and b.center_x < left_boundary]
# Sort by Y position
candidates.sort(key=lambda b: b.y0)
# Merge adjacent blocks
merged = []
current_group = []
for block in candidates:
if not current_group:
current_group.append(block)
elif should_merge(current_group[-1], block):
current_group.append(block)
else:
merged.append(merge_group(current_group))
current_group = [block]
if current_group:
merged.append(merge_group(current_group))
return merged
```
### Decision 3: Data Sources
**Primary source:** `cell_boxes` from PP-Structure
- Contains accurate geometric coordinates for each detected cell
- Independent of HTML structure recognition
**Secondary source:** HTML content with row/col attributes
- Contains text content and structure
- May have incorrect col assignments (the problem we're fixing)
**Correlation:** Match HTML cells to cell_boxes using IoU (Intersection over Union):
```python
def match_html_cell_to_cellbox(html_cell: HtmlCell, cell_boxes: List[BBox]) -> Optional[BBox]:
"""Find the cell_box that best matches this HTML cell's position."""
best_iou = 0
best_box = None
for box in cell_boxes:
iou = calculate_iou(html_cell.inferred_bbox, box)
if iou > best_iou:
best_iou = iou
best_box = box
return best_box if best_iou > 0.3 else None
```
## Configuration
```python
# config.py additions
table_column_correction_enabled: bool = Field(
default=True,
description="Enable header-anchor column correction"
)
table_column_correction_threshold: float = Field(
default=0.5,
description="Minimum X-overlap ratio to trigger column correction"
)
vertical_fragment_merge_enabled: bool = Field(
default=True,
description="Enable vertical text fragment merging"
)
vertical_fragment_aspect_ratio: float = Field(
default=0.3,
description="Max width/height ratio to consider as vertical text"
)
```
## Risks / Trade-offs
| Risk | Mitigation |
|------|------------|
| Headers themselves misaligned | Fall back to original column assignments |
| Multi-row headers | Support colspan detection in header extraction |
| Tables without headers | Skip correction, use original structure |
| Performance overhead | O(n*m) is negligible for typical table sizes |
## Integration Points
1. **Input:** PP-Structure's `table_res` containing:
- `cell_boxes`: List of [x0, y0, x1, y1] coordinates
- `html`: Table HTML with row/col attributes
2. **Output:** Corrected table structure with:
- Updated col indices in HTML cells
- Merged vertical text blocks
- Diagnostic logs for corrections made
3. **Trigger location:** After PP-Structure table recognition, before PDF generation
- File: `pdf_generator_service.py`
- Method: `draw_table_region()` or new preprocessing step
## Open Questions
1. **Q:** How to handle tables where header row itself is misaligned?
**A:** Could add a secondary validation using cell_boxes grid inference, but start simple.
2. **Q:** Should corrections be logged for user review?
**A:** Yes, add detailed logging with before/after column indices.

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# Change: Fix Table Column Alignment with Header-Anchor Correction
## Why
PP-Structure's table structure recognition frequently outputs cells with incorrect column indices, causing "column shift" where content appears in the wrong column. This happens because:
1. **Semantic over Geometric**: The model infers row/col from semantic patterns rather than physical coordinates
2. **Vertical text fragmentation**: Chinese vertical text (e.g., "报价内容") gets split into fragments
3. **Missing left boundary**: When table's left border is unclear, cells shift left incorrectly
The result: A cell with X-coordinate 213 gets assigned to column 0 (range 96-162) instead of column 1 (range 204-313).
## What Changes
- **Add Header-Anchor Alignment**: Use the first row (header) X-coordinates as column reference points
- **Add Coordinate-Based Column Correction**: Validate and correct cell column assignments based on X-coordinate overlap with header columns
- **Add Vertical Fragment Merging**: Detect and merge vertically stacked narrow text blocks that represent vertical text
- **Add Configuration Options**: Enable/disable correction features independently
## Impact
- Affected specs: `document-processing`
- Affected code:
- `backend/app/services/table_column_corrector.py` (new)
- `backend/app/services/pdf_generator_service.py`
- `backend/app/core/config.py`
## Problem Analysis
### Example: scan.pdf Table 7
**Raw PP-Structure Output:**
```
Row 5: "3、適應產品..." at X=213
Model says: col=0
Header Row 0:
- Column 0 (序號): X range [96, 162]
- Column 1 (產品名稱): X range [204, 313]
```
**Problem:** X=213 is far outside column 0's range (max 162), but perfectly within column 1's range (starts at 204).
**Solution:** Force-correct col=0 → col=1 based on X-coordinate alignment with header.
### Vertical Text Issue
**Raw OCR:**
```
Block A: "报价内" at X≈100, Y=[100, 200]
Block B: "容--" at X≈102, Y=[200, 300]
```
**Problem:** These should be one cell spanning multiple rows, but appear as separate fragments.
**Solution:** Merge vertically aligned narrow blocks before structure recognition.

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## ADDED Requirements
### Requirement: Table Column Alignment Correction
The system SHALL correct table cell column assignments using header-anchor alignment when PP-Structure outputs incorrect column indices.
#### Scenario: Correct column shift using header anchors
- **WHEN** processing a table with cell_boxes and HTML content
- **THEN** the system SHALL extract header row (row_idx=0) column X-coordinate ranges
- **AND** validate each cell's column assignment against header X-ranges
- **AND** correct column index if cell X-overlap with assigned column is < 50%
- **AND** assign cell to column with highest X-overlap
#### Scenario: Handle tables without headers
- **WHEN** processing a table without a clear header row
- **THEN** the system SHALL skip column correction
- **AND** use original PP-Structure column assignments
- **AND** log that header-anchor correction was skipped
#### Scenario: Log column corrections
- **WHEN** a cell's column index is corrected
- **THEN** the system SHALL log original and corrected column indices
- **AND** include cell content snippet for debugging
- **AND** record total corrections per table
### Requirement: Vertical Text Fragment Merging
The system SHALL detect and merge vertically fragmented Chinese text blocks that represent single cells spanning multiple rows.
#### Scenario: Detect vertical text fragments
- **WHEN** processing table text regions
- **THEN** the system SHALL identify narrow text blocks (width/height ratio < 0.3)
- **AND** filter blocks in leftmost 15% of table area
- **AND** group vertically adjacent blocks with X-center deviation < 10px
#### Scenario: Merge fragmented vertical text
- **WHEN** vertical text fragments are detected
- **THEN** the system SHALL merge adjacent fragments into single text blocks
- **AND** combine text content preserving reading order
- **AND** calculate merged bounding box spanning all fragments
- **AND** treat merged block as single cell for column assignment
#### Scenario: Preserve non-vertical text
- **WHEN** text blocks do not meet vertical fragment criteria
- **THEN** the system SHALL preserve original text block boundaries
- **AND** process normally without merging
## MODIFIED Requirements
### Requirement: Extract table structure
The system SHALL extract cell content and boundaries from PP-StructureV3 tables, with post-processing correction for column alignment errors.
#### Scenario: Extract table structure with correction
- **WHEN** PP-StructureV3 identifies a table
- **THEN** the system SHALL extract cell content and boundaries
- **AND** validate cell_boxes coordinates against page boundaries
- **AND** apply header-anchor column correction when enabled
- **AND** merge vertical text fragments when enabled
- **AND** apply fallback detection for invalid coordinates
- **AND** preserve table HTML for structure
- **AND** extract plain text for translation

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## 1. Core Algorithm Implementation
### 1.1 Table Column Corrector Module
- [x] 1.1.1 Create `table_column_corrector.py` service file
- [x] 1.1.2 Implement `ColumnAnchor` dataclass for header column ranges
- [x] 1.1.3 Implement `build_column_anchors()` to extract header column X-ranges
- [x] 1.1.4 Implement `calculate_x_overlap()` utility function
- [x] 1.1.5 Implement `correct_cell_column()` for single cell correction
- [x] 1.1.6 Implement `correct_table_columns()` main entry point
### 1.2 HTML Cell Extraction
- [x] 1.2.1 Implement `parse_table_html_with_positions()` to extract cells with row/col
- [x] 1.2.2 Implement cell-to-cellbox matching using IoU
- [x] 1.2.3 Handle colspan/rowspan in header detection
### 1.3 Vertical Fragment Merging
- [x] 1.3.1 Implement `detect_vertical_fragments()` to find narrow text blocks
- [x] 1.3.2 Implement `should_merge_blocks()` adjacency check
- [x] 1.3.3 Implement `merge_vertical_fragments()` main function
- [x] 1.3.4 Integrate merged blocks back into table structure
## 2. Configuration
### 2.1 Settings
- [x] 2.1.1 Add `table_column_correction_enabled: bool = True`
- [x] 2.1.2 Add `table_column_correction_threshold: float = 0.5`
- [x] 2.1.3 Add `vertical_fragment_merge_enabled: bool = True`
- [x] 2.1.4 Add `vertical_fragment_aspect_ratio: float = 0.3`
## 3. Integration
### 3.1 Pipeline Integration
- [x] 3.1.1 Add correction step in `pdf_generator_service.py` before table rendering
- [x] 3.1.2 Pass corrected HTML to existing table rendering logic
- [x] 3.1.3 Add diagnostic logging for corrections made
### 3.2 Error Handling
- [x] 3.2.1 Handle tables without headers gracefully
- [x] 3.2.2 Handle empty/malformed cell_boxes
- [x] 3.2.3 Fallback to original structure on correction failure
## 4. Testing
### 4.1 Unit Tests
- [ ] 4.1.1 Test `build_column_anchors()` with various header configurations
- [ ] 4.1.2 Test `correct_cell_column()` with known column shift cases
- [ ] 4.1.3 Test `merge_vertical_fragments()` with vertical text samples
- [ ] 4.1.4 Test edge cases: empty tables, single column, no headers
### 4.2 Integration Tests
- [ ] 4.2.1 Test with `scan.pdf` Table 7 (the problematic case)
- [ ] 4.2.2 Test with tables that have correct alignment (no regression)
- [ ] 4.2.3 Visual comparison of corrected vs original output
## 5. Documentation
- [x] 5.1 Add inline code comments explaining correction algorithm
- [x] 5.2 Update spec with new table column correction requirement
- [x] 5.3 Add logging messages for debugging

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# Change: Improve OCR Track Algorithm Based on PP-StructureV3 Best Practices
## Why
目前 OCR Track 的 Gap Filling 演算法使用 **IoU (Intersection over Union)** 判斷 OCR 文字是否被 Layout 區域覆蓋。根據 PaddleX 官方文件 (paddle_review.md) 建議，應改用 **IoA (Intersection over Area)** 才能正確判斷「小框是否被大框包含」的非對稱關係。此外，現行使用統一閾值處理所有元素類型，但不同類型應有不同閾值策略。
## What Changes
1. **IoU → IoA 演算法變更**: 將 `gap_filling_service.py` 中的覆蓋判定從 IoU 改為 IoA
2. **動態閾值策略**: 依元素類型 (TEXT, TABLE, FIGURE) 使用不同的 IoA 閾值
3. **使用 PP-StructureV3 內建 OCR**: 改用 `overall_ocr_res` 取代獨立執行 Raw OCR，節省推理時間並確保座標一致
4. **邊界收縮處理**: OCR 框內縮 1-2 px 避免邊緣重複渲染
## Impact
- Affected specs: `ocr-processing`
- Affected code:
- `backend/app/services/gap_filling_service.py` - 核心演算法變更
- `backend/app/services/ocr_service.py` - 改用 `overall_ocr_res`
- `backend/app/services/processing_orchestrator.py` - 調整 OCR 資料來源
- `backend/app/core/config.py` - 新增元素類型閾值設定
## Technical Details
### 1. IoA vs IoU
```
IoU = 交集面積 / 聯集面積 (對稱，用於判斷兩框是否指向同物體)
IoA = 交集面積 / OCR框面積 (非對稱，用於判斷小框是否被大框包含)
```
當 Layout 框遠大於 OCR 框時，IoU 會過小導致誤判為「未覆蓋」。
### 2. 動態閾值建議
| 元素類型 | IoA 閾值 | 說明 |
|---------|---------|------|
| TEXT/TITLE | 0.6 | 容忍邊界誤差 |
| TABLE | 0.1 | 嚴格過濾，避免破壞表格結構 |
| FIGURE | 0.8 | 保留圖中文字 (如軸標籤) |
### 3. overall_ocr_res 驗證結果
已確認 PP-StructureV3 的 `json['res']['overall_ocr_res']` 包含：
- `dt_polys`: 檢測框座標 (polygon 格式)
- `rec_texts`: 識別文字
- `rec_scores`: 識別信心度
測試結果顯示與獨立執行 Raw OCR 的結果數量相同 (59 regions)，可安全替換。

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## MODIFIED Requirements
### Requirement: OCR Track Gap Filling with Raw OCR Regions
The system SHALL detect and fill gaps in PP-StructureV3 output by supplementing with Raw OCR text regions when significant content loss is detected.
#### Scenario: Gap filling activates when coverage is low
- **GIVEN** an OCR track processing task
- **WHEN** PP-StructureV3 outputs elements that cover less than 70% of Raw OCR text regions
- **THEN** the system SHALL activate gap filling
- **AND** identify Raw OCR regions not covered by any PP-StructureV3 element
- **AND** supplement these regions as TEXT elements in the output
#### Scenario: Coverage is determined by IoA (Intersection over Area)
- **GIVEN** a Raw OCR text region with bounding box
- **WHEN** checking if the region is covered by PP-StructureV3
- **THEN** the region SHALL be considered covered if IoA (intersection area / OCR box area) exceeds the type-specific threshold
- **AND** IoA SHALL be used instead of IoU because it correctly measures "small box contained in large box" relationship
- **AND** regions not meeting the IoA criterion SHALL be marked as uncovered
#### Scenario: Element-type-specific IoA thresholds are applied
- **GIVEN** a Raw OCR region being evaluated for coverage
- **WHEN** comparing against PP-StructureV3 elements of different types
- **THEN** the system SHALL apply different IoA thresholds:
- TEXT, TITLE, HEADER, FOOTER: IoA > 0.6 (tolerates boundary errors)
- TABLE: IoA > 0.1 (strict filtering to preserve table structure)
- FIGURE, IMAGE: IoA > 0.8 (preserves text within figures like axis labels)
- **AND** a region is considered covered if it meets the threshold for ANY overlapping element
#### Scenario: Only TEXT elements are supplemented
- **GIVEN** uncovered Raw OCR regions identified for supplementation
- **WHEN** PP-StructureV3 has detected TABLE, IMAGE, FIGURE, FLOWCHART, HEADER, or FOOTER elements
- **THEN** the system SHALL NOT supplement regions that overlap with these structural elements
- **AND** only supplement regions as TEXT type to preserve structural integrity
#### Scenario: Supplemented regions meet confidence threshold
- **GIVEN** Raw OCR regions to be supplemented
- **WHEN** a region has confidence score below 0.3
- **THEN** the system SHALL skip that region
- **AND** only supplement regions with confidence >= 0.3
#### Scenario: Deduplication uses IoA instead of IoU
- **GIVEN** a Raw OCR region being considered for supplementation
- **WHEN** the region has IoA > 0.5 with any existing PP-StructureV3 TEXT element
- **THEN** the system SHALL skip that region to prevent duplicate text
- **AND** the original PP-StructureV3 element SHALL be preserved
#### Scenario: Reading order is recalculated after gap filling
- **GIVEN** supplemented elements have been added to the page
- **WHEN** assembling the final element list
- **THEN** the system SHALL recalculate reading order for the entire page
- **AND** sort elements by y0 coordinate (top to bottom) then x0 (left to right)
- **AND** ensure logical document flow is maintained
#### Scenario: Coordinate alignment with ocr_dimensions
- **GIVEN** Raw OCR processing may involve image resizing
- **WHEN** comparing Raw OCR bbox with PP-StructureV3 bbox
- **THEN** the system SHALL use ocr_dimensions to normalize coordinates
- **AND** ensure both sources reference the same coordinate space
- **AND** prevent coverage misdetection due to scale differences
#### Scenario: Supplemented elements have complete metadata
- **GIVEN** a Raw OCR region being added as supplemented element
- **WHEN** creating the DocumentElement
- **THEN** the element SHALL include page_number
- **AND** include confidence score from Raw OCR
- **AND** include original bbox coordinates
- **AND** optionally include source indicator for debugging
### Requirement: Gap Filling Configuration
The system SHALL provide configurable parameters for gap filling behavior.
#### Scenario: Gap filling can be disabled via configuration
- **GIVEN** gap_filling_enabled is set to false in configuration
- **WHEN** OCR track processing runs
- **THEN** the system SHALL skip all gap filling logic
- **AND** output only PP-StructureV3 results as before
#### Scenario: Coverage threshold is configurable
- **GIVEN** gap_filling_coverage_threshold is set to 0.8
- **WHEN** PP-StructureV3 coverage is 75%
- **THEN** the system SHALL activate gap filling
- **AND** supplement uncovered regions
#### Scenario: IoA thresholds are configurable per element type
- **GIVEN** custom IoA thresholds configured:
- gap_filling_ioa_threshold_text: 0.6
- gap_filling_ioa_threshold_table: 0.1
- gap_filling_ioa_threshold_figure: 0.8
- gap_filling_dedup_ioa_threshold: 0.5
- **WHEN** evaluating coverage and deduplication
- **THEN** the system SHALL use the configured values
- **AND** apply them consistently throughout gap filling process
#### Scenario: Confidence threshold is configurable
- **GIVEN** gap_filling_confidence_threshold is set to 0.5
- **WHEN** supplementing Raw OCR regions
- **THEN** the system SHALL only include regions with confidence >= 0.5
- **AND** filter out lower confidence regions
#### Scenario: Boundary shrinking reduces edge duplicates
- **GIVEN** gap_filling_shrink_pixels is set to 1
- **WHEN** evaluating coverage with IoA
- **THEN** the system SHALL shrink OCR bounding boxes inward by 1 pixel on each side
- **AND** this reduces false "uncovered" detection at region boundaries
## ADDED Requirements
### Requirement: Use PP-StructureV3 Internal OCR Results
The system SHALL preferentially use PP-StructureV3's internal OCR results (`overall_ocr_res`) instead of running a separate Raw OCR inference.
#### Scenario: Extract overall_ocr_res from PP-StructureV3
- **GIVEN** PP-StructureV3 processing completes
- **WHEN** the result contains `json['res']['overall_ocr_res']`
- **THEN** the system SHALL extract OCR regions from:
- `dt_polys`: detection box polygons
- `rec_texts`: recognized text strings
- `rec_scores`: confidence scores
- **AND** convert these to the standard TextRegion format for gap filling
#### Scenario: Skip separate Raw OCR when overall_ocr_res is available
- **GIVEN** gap_filling_use_overall_ocr is true (default)
- **WHEN** PP-StructureV3 result contains overall_ocr_res
- **THEN** the system SHALL NOT execute separate PaddleOCR inference
- **AND** use the extracted overall_ocr_res as the OCR source
- **AND** this reduces total inference time by approximately 50%
#### Scenario: Fallback to separate Raw OCR when needed
- **GIVEN** gap_filling_use_overall_ocr is false OR overall_ocr_res is missing
- **WHEN** gap filling is activated
- **THEN** the system SHALL execute separate PaddleOCR inference as before
- **AND** use the separate OCR results for gap filling
- **AND** this maintains backward compatibility
#### Scenario: Coordinate consistency is guaranteed
- **GIVEN** overall_ocr_res is extracted from PP-StructureV3
- **WHEN** comparing with PP-StructureV3 layout elements
- **THEN** both SHALL use the same coordinate system
- **AND** no additional coordinate alignment is needed
- **AND** this prevents scale mismatch issues

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## 1. Algorithm Changes (gap_filling_service.py)
### 1.1 IoA Implementation
- [x] 1.1.1 Add `_calculate_ioa()` method alongside existing `_calculate_iou()`
- [x] 1.1.2 Modify `_is_region_covered()` to use IoA instead of IoU
- [x] 1.1.3 Update deduplication logic to use IoA
### 1.2 Dynamic Threshold Strategy
- [x] 1.2.1 Add element-type-specific thresholds as class constants
- [x] 1.2.2 Modify `_is_region_covered()` to accept element type parameter
- [x] 1.2.3 Apply different thresholds based on element type (TEXT: 0.6, TABLE: 0.1, FIGURE: 0.8)
### 1.3 Boundary Shrinking
- [x] 1.3.1 Add optional `shrink_pixels` parameter to coverage detection
- [x] 1.3.2 Implement bbox shrinking logic (inward 1-2 px)
## 2. OCR Data Source Changes
### 2.1 Extract overall_ocr_res from PP-StructureV3
- [x] 2.1.1 Modify `pp_structure_enhanced.py` to extract `overall_ocr_res` from result
- [x] 2.1.2 Convert `dt_polys` + `rec_texts` + `rec_scores` to TextRegion format
- [x] 2.1.3 Store extracted OCR in result dict for gap filling
### 2.2 Update Processing Orchestrator
- [x] 2.2.1 Add option to use `overall_ocr_res` as OCR source
- [x] 2.2.2 Skip separate Raw OCR inference when using PP-StructureV3's OCR
- [x] 2.2.3 Maintain backward compatibility with explicit Raw OCR mode
## 3. Configuration Updates
### 3.1 Add Settings (config.py)
- [x] 3.1.1 Add `gap_filling_ioa_threshold_text: float = 0.6`
- [x] 3.1.2 Add `gap_filling_ioa_threshold_table: float = 0.1`
- [x] 3.1.3 Add `gap_filling_ioa_threshold_figure: float = 0.8`
- [x] 3.1.4 Add `gap_filling_use_overall_ocr: bool = True`
- [x] 3.1.5 Add `gap_filling_shrink_pixels: int = 1`
## 4. Testing
### 4.1 Unit Tests
- [ ] 4.1.1 Test IoA calculation with known values
- [ ] 4.1.2 Test dynamic threshold selection by element type
- [ ] 4.1.3 Test boundary shrinking edge cases
### 4.2 Integration Tests
- [ ] 4.2.1 Test with scan.pdf (current problematic file)
- [ ] 4.2.2 Compare results: old IoU vs new IoA approach
- [ ] 4.2.3 Verify no duplicate text rendering in output PDF
- [ ] 4.2.4 Verify table content is not duplicated outside table bounds
## 5. Documentation
- [x] 5.1 Update spec documentation with new algorithm
- [x] 5.2 Add inline code comments explaining IoA vs IoU

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# Change: Remove Unused Code and Legacy Files
## Why
專案經過多次迭代開發後，累積了一些未使用的代碼和遺留文件。這些冗餘代碼增加了維護負擔、可能造成混淆，並佔用不必要的存儲空間。本提案旨在系統性地移除這些未使用的代碼，以達成專案內容及程式代碼的精簡。
## What Changes
### Backend - 移除未使用的服務文件 (3個)
| 文件 | 行數 | 移除原因 |
|------|------|----------|
| `ocr_service_original.py` | ~835 | 舊版 OCR 服務，已被 `ocr_service.py` 完全取代 |
| `preprocessor.py` | ~200 | 文檔預處理器，功能已被 `layout_preprocessing_service.py` 吸收 |
| `pdf_font_manager.py` | ~150 | 字體管理器，未被任何服務引用 |
### Frontend - 移除未使用的組件 (2個)
| 文件 | 移除原因 |
|------|----------|
| `MarkdownPreview.tsx` | 完全未被任何頁面或組件引用 |
| `ResultsTable.tsx` | 使用已棄用的 `FileResult` 類型，功能已被 `TaskHistoryPage` 替代 |
### Frontend - 遷移並移除遺留 API 服務 (2個)
| 文件 | 移除原因 |
|------|----------|
| `services/api.ts` | 舊版 API 客戶端，僅剩 2 處引用 (Layout.tsx, SettingsPage.tsx)，需遷移至 apiV2 |
| `types/api.ts` | 舊版類型定義，僅 `ExportRule` 類型被使用，需遷移至 apiV2.ts |
## Impact
- **Affected specs**: 無 (純代碼清理，不改變系統行為)
- **Affected code**:
- Backend: `backend/app/services/` (刪除 3 個文件)
- Frontend: `frontend/src/components/` (刪除 2 個文件)
- Frontend: `frontend/src/services/api.ts` (遷移後刪除)
- Frontend: `frontend/src/types/api.ts` (遷移後刪除)
## Benefits
- 減少約 1,200+ 行後端冗餘代碼
- 減少約 300+ 行前端冗餘代碼
- 提高代碼維護性和可讀性
- 消除新開發者的混淆源
- 統一 API 客戶端到 apiV2
## Risk Assessment
- **風險等級**: 低
- **回滾策略**: Git revert 即可恢復所有刪除的文件
- **測試要求**:
- 確認後端服務啟動正常
- 確認前端所有頁面功能正常
- 特別測試 SettingsPage (ExportRule) 功能

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## REMOVED Requirements
### Requirement: Legacy OCR Service Implementation
**Reason**: `ocr_service_original.py` was the original OCR service implementation that has been completely superseded by the current `ocr_service.py`. The legacy file is no longer referenced by any part of the codebase.
**Migration**: No migration needed. The current `ocr_service.py` provides all required functionality with improved architecture.
#### Scenario: Legacy service file removal
- **WHEN** the legacy `ocr_service_original.py` file is removed
- **THEN** the system continues to function normally using `ocr_service.py`
- **AND** no import errors occur in any service or router
### Requirement: Unused Preprocessor Service
**Reason**: `preprocessor.py` was a document preprocessor that is no longer used. Its functionality has been absorbed by `layout_preprocessing_service.py`.
**Migration**: No migration needed. The preprocessing functionality is available through `layout_preprocessing_service.py`.
#### Scenario: Preprocessor file removal
- **WHEN** the unused `preprocessor.py` file is removed
- **THEN** the system continues to function normally
- **AND** layout preprocessing works correctly via `layout_preprocessing_service.py`
### Requirement: Unused PDF Font Manager
**Reason**: `pdf_font_manager.py` was intended for font management but is not referenced by `pdf_generator_service.py` or any other service.
**Migration**: No migration needed. Font handling is managed within `pdf_generator_service.py` directly.
#### Scenario: Font manager file removal
- **WHEN** the unused `pdf_font_manager.py` file is removed
- **THEN** PDF generation continues to work correctly
- **AND** fonts are rendered properly in generated PDFs
### Requirement: Legacy Frontend Components
**Reason**: `MarkdownPreview.tsx` and `ResultsTable.tsx` are frontend components that are not referenced by any page or component in the application.
**Migration**: No migration needed. `MarkdownPreview` functionality is not currently used. `ResultsTable` functionality has been replaced by `TaskHistoryPage`.
#### Scenario: Unused frontend component removal
- **WHEN** the unused `MarkdownPreview.tsx` and `ResultsTable.tsx` files are removed
- **THEN** the frontend application compiles successfully
- **AND** all pages render and function correctly
### Requirement: Legacy API Client Migration
**Reason**: `services/api.ts` and `types/api.ts` are legacy API client files with only 2 remaining references. These should be migrated to `apiV2` for consistency.
**Migration**:
1. Move `ExportRule` type to `types/apiV2.ts`
2. Add export rules API functions to `services/apiV2.ts`
3. Update `SettingsPage.tsx` and `Layout.tsx` to use apiV2
4. Remove legacy api.ts files
#### Scenario: Legacy API client removal after migration
- **WHEN** the legacy `api.ts` files are removed after migration
- **THEN** all API calls use the unified `apiV2` client
- **AND** `SettingsPage` export rules functionality works correctly
- **AND** `Layout` logout functionality works correctly

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# Tasks: Remove Unused Code and Legacy Files
## Phase 1: Backend Cleanup (無依賴，可直接刪除)
- [ ] 1.1 確認 `ocr_service_original.py` 無任何引用
- [ ] 1.2 刪除 `backend/app/services/ocr_service_original.py`
- [ ] 1.3 確認 `preprocessor.py` 無任何引用
- [ ] 1.4 刪除 `backend/app/services/preprocessor.py`
- [ ] 1.5 確認 `pdf_font_manager.py` 無任何引用
- [ ] 1.6 刪除 `backend/app/services/pdf_font_manager.py`
- [ ] 1.7 測試後端服務啟動正常
## Phase 2: Frontend Unused Components (無依賴，可直接刪除)
- [ ] 2.1 確認 `MarkdownPreview.tsx` 無任何引用
- [ ] 2.2 刪除 `frontend/src/components/MarkdownPreview.tsx`
- [ ] 2.3 確認 `ResultsTable.tsx` 無任何引用
- [ ] 2.4 刪除 `frontend/src/components/ResultsTable.tsx`
- [ ] 2.5 測試前端編譯正常
## Phase 3: Frontend API Migration (需先遷移再刪除)
- [ ] 3.1 將 `ExportRule` 類型從 `types/api.ts` 遷移到 `types/apiV2.ts`
- [ ] 3.2 在 `services/apiV2.ts` 中添加 export rules 相關 API 函數
- [ ] 3.3 更新 `SettingsPage.tsx` 使用 apiV2 的 ExportRule
- [ ] 3.4 更新 `Layout.tsx` 移除對 api.ts 的依賴
- [ ] 3.5 確認 `services/api.ts` 無任何引用
- [ ] 3.6 刪除 `frontend/src/services/api.ts`
- [ ] 3.7 確認 `types/api.ts` 無任何引用
- [ ] 3.8 刪除 `frontend/src/types/api.ts`
- [ ] 3.9 測試前端所有功能正常
## Phase 4: Verification
- [ ] 4.1 運行後端測試 (如有)
- [ ] 4.2 運行前端編譯 `npm run build`
- [ ] 4.3 手動測試關鍵功能:
- [ ] 登入/登出
- [ ] 文件上傳
- [ ] OCR 處理
- [ ] 結果查看
- [ ] 導出設定頁面
- [ ] 4.4 確認無 console 錯誤或警告

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# Design: Simple Text Positioning
## Architecture
### Current Flow (Complex)
```
Raw OCR → PP-Structure Analysis → Table Detection → HTML Parsing →
Column Correction → Cell Positioning → PDF Generation
```
### New Flow (Simple)
```
Raw OCR → Text Region Extraction → Bbox Processing →
Rotation Calculation → Font Size Estimation → PDF Text Rendering
```
## Core Components
### 1. TextRegionRenderer
New service class to handle raw OCR text rendering:
```python
class TextRegionRenderer:
"""Render raw OCR text regions to PDF."""
def render_text_region(
self,
canvas: Canvas,
region: Dict,
scale_factor: float
) -> None:
"""
Render a single OCR text region.
Args:
canvas: ReportLab canvas
region: Raw OCR region with text and bbox
scale_factor: Coordinate scaling factor
"""
```
### 2. Bbox Processing
Raw OCR bbox format (quadrilateral - 4 corner points):
```json
{
"text": "LOCTITE",
"bbox": [[116, 76], [378, 76], [378, 128], [116, 128]],
"confidence": 0.98
}
```
Processing steps:
1. **Center point**: Average of 4 corners
2. **Width/Height**: Distance between corners
3. **Rotation angle**: Angle of top edge from horizontal
4. **Font size**: Approximate from bbox height
### 3. Rotation Calculation
```python
def calculate_rotation(bbox: List[List[float]]) -> float:
"""
Calculate text rotation from bbox quadrilateral.
Returns angle in degrees (counter-clockwise from horizontal).
"""
# Top-left to top-right vector
dx = bbox[1][0] - bbox[0][0]
dy = bbox[1][1] - bbox[0][1]
# Angle in degrees
angle = math.atan2(dy, dx) * 180 / math.pi
return angle
```
### 4. Font Size Estimation
```python
def estimate_font_size(bbox: List[List[float]], text: str) -> float:
"""
Estimate font size from bbox dimensions.
Uses bbox height as primary indicator, adjusted for aspect ratio.
"""
# Calculate bbox height (average of left and right edges)
left_height = math.dist(bbox[0], bbox[3])
right_height = math.dist(bbox[1], bbox[2])
avg_height = (left_height + right_height) / 2
# Font size is approximately 70-80% of bbox height
return avg_height * 0.75
```
## Integration Points
### PDFGeneratorService
Modify `draw_ocr_content()` to use simple text positioning:
```python
def draw_ocr_content(self, canvas, content_data, page_info):
"""Draw OCR content using simple text positioning."""
# Use raw OCR regions directly
raw_regions = content_data.get('raw_ocr_regions', [])
for region in raw_regions:
self.text_renderer.render_text_region(
canvas, region, scale_factor
)
```
### Configuration
Add config option to enable/disable simple mode:
```python
class OCRSettings:
simple_text_positioning: bool = Field(
default=True,
description="Use simple text positioning instead of table reconstruction"
)
```
## File Changes
| File | Change |
|------|--------|
| `app/services/text_region_renderer.py` | New - Text rendering logic |
| `app/services/pdf_generator_service.py` | Modify - Integration |
| `app/core/config.py` | Add - Configuration option |
## Edge Cases
1. **Overlapping text**: Regions may overlap slightly - render in reading order
2. **Very small text**: Minimum font size threshold (6pt)
3. **Rotated pages**: Handle 90/180/270 degree page rotation
4. **Empty regions**: Skip regions with empty text
5. **Unicode text**: Ensure font supports CJK characters

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# Simple Text Positioning from Raw OCR
## Summary
Simplify OCR track PDF generation by rendering raw OCR text at correct positions without complex table structure reconstruction.
## Problem
Current OCR track processing has multiple failure points:
1. PP-Structure table structure recognition fails for borderless tables
2. Multi-column layouts get merged incorrectly into single tables
3. Table HTML reconstruction produces wrong cell positions
4. Complex column correction algorithms still can't fix fundamental structure errors
Meanwhile, raw OCR (`raw_ocr_regions.json`) correctly identifies all text with accurate bounding boxes.
## Solution
Replace complex table reconstruction with simple text positioning:
1. Read raw OCR regions directly
2. Position text at bbox coordinates
3. Calculate text rotation from bbox quadrilateral shape
4. Estimate font size from bbox height
5. Skip table HTML parsing entirely for OCR track
## Benefits
- **Reliability**: Raw OCR text positions are accurate
- **Simplicity**: Eliminates complex table parsing logic
- **Performance**: Faster processing without structure analysis
- **Consistency**: Predictable output regardless of table type
## Trade-offs
- No table borders in output
- No cell structure (colspan, rowspan)
- Visual layout approximation rather than semantic structure
## Scope
- OCR track PDF generation only
- Direct track remains unchanged (uses native PDF text extraction)

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# Tasks: Simple Text Positioning
## Phase 1: Core Implementation
- [x] Create `TextRegionRenderer` class in `app/services/text_region_renderer.py`
- [x] Implement `calculate_rotation()` from bbox quadrilateral
- [x] Implement `estimate_font_size()` from bbox height
- [x] Implement `render_text_region()` main method
- [x] Handle coordinate system transformation (OCR → PDF)
## Phase 2: Integration
- [x] Add `simple_text_positioning_enabled` config option
- [x] Modify `PDFGeneratorService._generate_ocr_track_pdf()` to use `TextRegionRenderer`
- [x] Ensure raw OCR regions are loaded correctly via `load_raw_ocr_regions()`
## Phase 3: Image/Chart/Formula Support
- [x] Add image element type detection (`figure`, `image`, `chart`, `seal`, `formula`)
- [x] Render image elements from UnifiedDocument to PDF
- [x] Handle image path resolution (result_dir, imgs/ subdirectory)
- [x] Coordinate transformation for image placement
## Phase 4: Text Straightening & Overlap Avoidance
- [x] Add rotation straightening threshold (default 10°)
- Small rotation angles (< 10°) are treated as 0° for clean output
- Only significant rotations (e.g., 90°) are preserved
- [x] Add IoA (Intersection over Area) overlap detection
- IoA threshold default 0.3 (30% overlap triggers skip)
- Text regions overlapping with images/charts are skipped
- [x] Collect exclusion zones from image elements
- [x] Pass exclusion zones to text renderer
## Phase 5: Chart Axis Label Deduplication
- [x] Add `is_axis_label()` method to detect axis labels
- Y-axis: Vertical text immediately left of chart
- X-axis: Horizontal text immediately below chart
- [x] Add `is_near_zone()` method for proximity checking
- [x] Position-aware deduplication in `render_text_region()`
- Collect texts inside zones + axis labels
- Skip matching text only if near zone or is axis label
- Preserve matching text far from zones (e.g., table values)
- [x] Test results:
- "Temperature, C" and "Syringe Thaw Time, Minutes" correctly skipped
- Table values like "10" at top of page correctly rendered
- Page 2: 128/148 text regions rendered (12 overlap + 8 dedupe)
## Phase 6: Testing
- [x] Test with scan.pdf task (064e2d67-338c-4e54-b005-204c3b76fe63)
- Page 2: Chart image rendered, axis labels deduplicated
- PDF is searchable and selectable
- Text is properly straightened (no skew artifacts)
- [ ] Compare output quality vs original scan visually
- [ ] Test with documents containing seals/formulas

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# Design: cell_boxes-First Table Rendering
## Architecture Overview
```
┌─────────────────────────────────────────────────────────────────┐
│ Table Rendering Pipeline │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Input: table_element │
│ ├── cell_boxes: [[x0,y0,x1,y1], ...] (from PP-StructureV3)│
│ ├── html: "<table>...</table>" (from PP-StructureV3)│
│ └── bbox: [x0, y0, x1, y1] (table boundary) │
│ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Step 1: Grid Inference from cell_boxes │ │
│ │ │ │
│ │ cell_boxes → cluster by Y → rows │ │
│ │ → cluster by X → cols │ │
│ │ → build grid[row][col] = cell_bbox │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Step 2: Content Extraction from HTML │ │
│ │ │ │
│ │ html → parse → extract text list in reading order │ │
│ │ → flatten colspan/rowspan → [text1, text2, ...] │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Step 3: Content-to-Cell Mapping │ │
│ │ │ │
│ │ Option A: Sequential assignment (text[i] → cell[i]) │ │
│ │ Option B: Coordinate matching (text_bbox ∩ cell_bbox) │ │
│ │ Option C: Row-by-row assignment │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Step 4: PDF Rendering │ │
│ │ │ │
│ │ For each cell in grid: │ │
│ │ 1. Draw cell border at cell_bbox coordinates │ │
│ │ 2. Render text content inside cell │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ Output: Table rendered in PDF with accurate cell boundaries │
└─────────────────────────────────────────────────────────────────┘
```
## Detailed Design
### 1. Grid Inference Algorithm
```python
def infer_grid_from_cellboxes(cell_boxes: List[List[float]], threshold: float = 15.0):
"""
Infer row/column grid structure from cell_boxes coordinates.
Args:
cell_boxes: List of [x0, y0, x1, y1] coordinates
threshold: Clustering threshold for row/column grouping
Returns:
grid: Dict[Tuple[int,int], Dict] mapping (row, col) to cell info
row_heights: List of row heights
col_widths: List of column widths
"""
# 1. Extract all Y-centers and X-centers
y_centers = [(cb[1] + cb[3]) / 2 for cb in cell_boxes]
x_centers = [(cb[0] + cb[2]) / 2 for cb in cell_boxes]
# 2. Cluster Y-centers into rows
rows = cluster_values(y_centers, threshold) # Returns sorted list of row indices
# 3. Cluster X-centers into columns
cols = cluster_values(x_centers, threshold) # Returns sorted list of col indices
# 4. Assign each cell_box to (row, col)
grid = {}
for i, cb in enumerate(cell_boxes):
row = find_cluster(y_centers[i], rows)
col = find_cluster(x_centers[i], cols)
grid[(row, col)] = {
'bbox': cb,
'index': i
}
# 5. Calculate actual widths/heights from boundaries
row_heights = [rows[i+1] - rows[i] for i in range(len(rows)-1)]
col_widths = [cols[i+1] - cols[i] for i in range(len(cols)-1)]
return grid, row_heights, col_widths
```
### 2. Content Extraction
The HTML content extraction should handle colspan/rowspan by flattening:
```python
def extract_cell_contents(html: str) -> List[str]:
"""
Extract cell text contents from HTML in reading order.
Expands colspan/rowspan into repeated empty strings.
Returns:
List of text strings, one per logical cell position
"""
parser = HTMLTableParser()
parser.feed(html)
contents = []
for row in parser.tables[0]['rows']:
for cell in row['cells']:
contents.append(cell['text'])
# For colspan > 1, add empty strings for merged cells
for _ in range(cell.get('colspan', 1) - 1):
contents.append('')
return contents
```
### 3. Content-to-Cell Mapping Strategy
**Recommended: Row-by-row Sequential Assignment**
Since HTML content is in reading order (top-to-bottom, left-to-right), map content to grid cells in the same order:
```python
def map_content_to_grid(grid, contents, num_rows, num_cols):
"""
Map extracted content to grid cells row by row.
"""
content_idx = 0
for row in range(num_rows):
for col in range(num_cols):
if (row, col) in grid:
if content_idx < len(contents):
grid[(row, col)]['content'] = contents[content_idx]
content_idx += 1
else:
grid[(row, col)]['content'] = ''
return grid
```
### 4. PDF Rendering Integration
Modify `pdf_generator_service.py` to use cell_boxes-first path:
```python
def draw_table_region(self, ...):
cell_boxes = table_element.get('cell_boxes', [])
html_content = table_element.get('content', '')
if cell_boxes and settings.table_rendering_prefer_cellboxes:
# Try cell_boxes-first approach
grid, row_heights, col_widths = infer_grid_from_cellboxes(cell_boxes)
if grid:
# Extract content from HTML
contents = extract_cell_contents(html_content)
# Map content to grid
grid = map_content_to_grid(grid, contents, len(row_heights), len(col_widths))
# Render using cell_boxes coordinates
success = self._render_table_from_grid(
pdf_canvas, grid, row_heights, col_widths,
page_height, scale_w, scale_h
)
if success:
return # Done
# Fallback to existing HTML-based rendering
self._render_table_from_html(...)
```
## Configuration
```python
# config.py
class Settings:
# Table rendering strategy
table_rendering_prefer_cellboxes: bool = Field(
default=True,
description="Use cell_boxes coordinates as primary table structure source"
)
table_cellboxes_row_threshold: float = Field(
default=15.0,
description="Y-coordinate threshold for row clustering"
)
table_cellboxes_col_threshold: float = Field(
default=15.0,
description="X-coordinate threshold for column clustering"
)
```
## Edge Cases
### 1. Empty cell_boxes
- **Condition**: `cell_boxes` is empty or None
- **Action**: Fall back to HTML-based rendering
### 2. Content Count Mismatch
- **Condition**: HTML has more/fewer cells than cell_boxes grid
- **Action**: Fill available cells, leave extras empty, log warning
### 3. Overlapping cell_boxes
- **Condition**: Multiple cell_boxes map to same grid position
- **Action**: Use first one, log warning
### 4. Single-cell Tables
- **Condition**: Only 1 cell_box detected
- **Action**: Render as single-cell table (valid case)
## Testing Plan
1. **Unit Tests**
- `test_infer_grid_from_cellboxes`: Various cell_box configurations
- `test_content_mapping`: Content assignment scenarios
2. **Integration Tests**
- `test_scan_pdf_table_7`: Verify the problematic table renders correctly
- `test_existing_tables`: No regression on previously working tables
3. **Visual Verification**
- Compare PDF output before/after for `scan.pdf`
- Check table alignment and text placement

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# Proposal: Use cell_boxes as Primary Table Rendering Source
## Summary
Modify table PDF rendering to use `cell_boxes` coordinates as the primary source for table structure instead of relying on HTML table parsing. This resolves grid mismatch issues where PP-StructureV3's HTML structure (with colspan/rowspan) doesn't match the cell_boxes coordinate grid.
## Problem Statement
### Current Issue
When processing `scan.pdf`, PP-StructureV3 detected tables with the following characteristics:
**Table 7 (Element 7)**:
- `cell_boxes`: 27 cells forming an 11x10 grid (by coordinate clustering)
- HTML structure: 9 rows with irregular columns `[7, 7, 1, 3, 3, 3, 3, 3, 1]` due to colspan
This **grid mismatch** causes:
1. `_compute_table_grid_from_cell_boxes()` returns `None, None`
2. PDF generator falls back to ReportLab Table with equal column distribution
3. Table renders with incorrect column widths, causing visual misalignment
### Root Cause
PP-StructureV3 sometimes merges multiple visual tables into one large table region:
- The cell_boxes accurately detect individual cell boundaries
- The HTML uses colspan to represent merged cells, but the grid doesn't match cell_boxes
- Current logic requires exact grid match, which fails for complex merged tables
## Proposed Solution
### Strategy: cell_boxes-First Rendering
Instead of requiring HTML grid to match cell_boxes, **use cell_boxes directly** as the authoritative source for cell boundaries:
1. **Grid Inference from cell_boxes**
- Cluster cell_boxes by Y-coordinate to determine rows
- Cluster cell_boxes by X-coordinate to determine columns
- Build a row×col grid map from cell_boxes positions
2. **Content Assignment from HTML**
- Extract text content from HTML in reading order
- Map text content to cell_boxes positions using coordinate matching
- Handle cases where HTML has fewer/more cells than cell_boxes
3. **Direct PDF Rendering**
- Render table borders using cell_boxes coordinates (already implemented)
- Place text content at calculated cell positions
- Skip ReportLab Table parsing when cell_boxes grid is valid
### Key Changes
| Component | Change |
|-----------|--------|
| `pdf_generator_service.py` | Add cell_boxes-first rendering path |
| `table_content_rebuilder.py` | Enhance to support grid-based content mapping |
| `config.py` | Add `table_rendering_prefer_cellboxes: bool` setting |
## Benefits
1. **Accurate Table Borders**: cell_boxes from ML detection are more precise than HTML parsing
2. **Handles Grid Mismatch**: Works even when HTML colspan/rowspan don't match cell count
3. **Consistent Output**: Same rendering logic regardless of HTML complexity
4. **Backward Compatible**: Existing HTML-based rendering remains as fallback
## Non-Goals
- Not modifying PP-StructureV3 detection logic
- Not implementing table splitting (separate proposal if needed)
- Not changing Direct track (PyMuPDF) table extraction
## Success Criteria
1. `scan.pdf` Table 7 renders with correct column widths based on cell_boxes
2. All existing table tests continue to pass
3. No regression for tables where HTML grid matches cell_boxes

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# document-processing Specification Delta
## MODIFIED Requirements
### Requirement: Extract table structure (Modified)
The system SHALL use cell_boxes coordinates as the primary source for table structure when rendering PDFs, with HTML parsing as fallback.
#### Scenario: Render table using cell_boxes grid
- **WHEN** rendering a table element to PDF
- **AND** the table has valid cell_boxes coordinates
- **AND** `table_rendering_prefer_cellboxes` is enabled
- **THEN** the system SHALL infer row/column grid from cell_boxes coordinates
- **AND** extract text content from HTML in reading order
- **AND** map content to grid cells by position
- **AND** render table borders using cell_boxes coordinates
- **AND** place text content within calculated cell boundaries
#### Scenario: Handle cell_boxes grid mismatch gracefully
- **WHEN** cell_boxes grid has different dimensions than HTML colspan/rowspan structure
- **THEN** the system SHALL use cell_boxes grid as authoritative structure
- **AND** map available HTML content to cells row-by-row
- **AND** leave unmapped cells empty
- **AND** log warning if content count differs significantly
#### Scenario: Fallback to HTML-based rendering
- **WHEN** cell_boxes is empty or None
- **OR** `table_rendering_prefer_cellboxes` is disabled
- **OR** cell_boxes grid inference fails
- **THEN** the system SHALL fall back to existing HTML-based table rendering
- **AND** use ReportLab Table with parsed HTML structure
#### Scenario: Maintain backward compatibility
- **WHEN** processing tables where cell_boxes grid matches HTML structure
- **THEN** the system SHALL produce identical output to previous behavior
- **AND** pass all existing table rendering tests

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## 1. Core Algorithm Implementation
### 1.1 Grid Inference Module
- [x] 1.1.1 Create `CellBoxGridInferrer` class in `pdf_table_renderer.py`
- [x] 1.1.2 Implement `cluster_values()` for Y/X coordinate clustering
- [x] 1.1.3 Implement `infer_grid_from_cellboxes()` main method
- [x] 1.1.4 Add row_heights and col_widths calculation
### 1.2 Content Mapping
- [x] 1.2.1 Implement `extract_cell_contents()` from HTML
- [x] 1.2.2 Implement `map_content_to_grid()` for row-by-row assignment
- [x] 1.2.3 Handle content count mismatch (more/fewer cells)
## 2. PDF Generator Integration
### 2.1 New Rendering Path
- [x] 2.1.1 Add `render_from_cellboxes_grid()` method to TableRenderer
- [x] 2.1.2 Integrate into `draw_table_region()` with cellboxes-first check
- [x] 2.1.3 Maintain fallback to existing HTML-based rendering
### 2.2 Cell Rendering
- [x] 2.2.1 Draw cell borders using cell_boxes coordinates
- [x] 2.2.2 Render text content with proper alignment and padding
- [x] 2.2.3 Handle multi-line text within cells
## 3. Configuration
### 3.1 Settings
- [x] 3.1.1 Add `table_rendering_prefer_cellboxes: bool = True`
- [x] 3.1.2 Add `table_cellboxes_row_threshold: float = 15.0`
- [x] 3.1.3 Add `table_cellboxes_col_threshold: float = 15.0`
## 4. Testing
### 4.1 Unit Tests
- [x] 4.1.1 Test grid inference with various cell_box configurations
- [x] 4.1.2 Test content mapping edge cases
- [x] 4.1.3 Test coordinate clustering accuracy
### 4.2 Integration Tests
- [ ] 4.2.1 Test with `scan.pdf` Table 7 (the problematic case)
- [ ] 4.2.2 Verify no regression on existing table tests
- [ ] 4.2.3 Visual comparison of output PDFs
## 5. Documentation
- [x] 5.1 Update inline code comments
- [x] 5.2 Update spec with new table rendering requirement

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@@ -67,7 +67,7 @@ The system SHALL use a standardized UnifiedDocument model as the common output f
- **AND** support identical downstream operations (PDF generation, translation)
### Requirement: Enhanced OCR with Full PP-StructureV3
The system SHALL utilize the full capabilities of PP-StructureV3, extracting all 23 element types from parsing_res_list.
The system SHALL utilize the full capabilities of PP-StructureV3, extracting all 23 element types from parsing_res_list, with proper handling of visual elements and table coordinates.
#### Scenario: Extract comprehensive document structure
- **WHEN** processing through OCR track
@@ -84,9 +84,17 @@ The system SHALL utilize the full capabilities of PP-StructureV3, extracting all
#### Scenario: Extract table structure
- **WHEN** PP-StructureV3 identifies a table
- **THEN** the system SHALL extract cell content and boundaries
- **AND** validate cell_boxes coordinates against page boundaries
- **AND** apply fallback detection for invalid coordinates
- **AND** preserve table HTML for structure
- **AND** extract plain text for translation
#### Scenario: Extract visual elements with paths
- **WHEN** PP-StructureV3 identifies visual elements (IMAGE, FIGURE, CHART, DIAGRAM)
- **THEN** the system SHALL preserve saved_path for each element
- **AND** include image dimensions and format
- **AND** enable image embedding in output PDF
### Requirement: Structure-Preserving Translation Foundation
The system SHALL maintain document structure and layout information to support future translation features.
@@ -108,3 +116,26 @@ The system SHALL maintain document structure and layout information to support f
- **AND** calculate maximum text expansion ratios
- **AND** preserve non-translatable elements (logos, signatures)
### Requirement: Generate UnifiedDocument from direct extraction
The system SHALL convert PyMuPDF results to UnifiedDocument with correct table cell merging.
#### Scenario: Extract tables with cell merging
- **WHEN** direct extraction encounters a table
- **THEN** the system SHALL use PyMuPDF find_tables() API
- **AND** extract cell content with correct rowspan/colspan
- **AND** preserve merged cell boundaries
- **AND** skip placeholder cells covered by merges
#### Scenario: Filter decoration images
- **WHEN** extracting images from PDF
- **THEN** the system SHALL filter images smaller than minimum area threshold
- **AND** exclude covering/redaction images
- **AND** preserve meaningful content images
#### Scenario: Preserve text styling with image handling
- **WHEN** direct extraction completes
- **THEN** the system SHALL convert PyMuPDF results to UnifiedDocument
- **AND** preserve text styling, fonts, and exact positioning
- **AND** extract tables with cell boundaries, content, and merge info
- **AND** include only meaningful images in output

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@@ -195,3 +195,66 @@ The system SHALL provide documentation for cleaning up unused model caches to op
- **THEN** the documentation SHALL explain how to delete unused cached models from `~/.paddlex/official_models/`
- **AND** list which model directories can be safely removed
### Requirement: Cell Over-Detection Filtering
The system SHALL validate PP-StructureV3 table detections using metric-based heuristics to filter over-detected cells.
#### Scenario: Cell density exceeds threshold
- **GIVEN** a table detected by PP-StructureV3 with cell_boxes
- **WHEN** cell density exceeds 3.0 cells per 10,000 px²
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Average cell area below threshold
- **GIVEN** a table detected by PP-StructureV3
- **WHEN** average cell area is less than 3,000 px²
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Cell height too small
- **GIVEN** a table with height H and N cells
- **WHEN** (H / N) is less than 10 pixels
- **THEN** the system SHALL flag the table as over-detected
- **AND** reclassify the table as a TEXT element
#### Scenario: Valid tables are preserved
- **GIVEN** a table with normal metrics (density < 3.0, avg area > 3000, height/N > 10)
- **WHEN** validation is applied
- **THEN** the table SHALL be preserved unchanged
- **AND** all cell_boxes SHALL be retained
### Requirement: Table-to-Text Reclassification
The system SHALL convert over-detected tables to TEXT elements while preserving content.
#### Scenario: Table content is preserved
- **GIVEN** a table flagged for reclassification
- **WHEN** converting to TEXT element
- **THEN** the system SHALL extract text content from table HTML
- **AND** preserve the original bounding box
- **AND** set element type to TEXT
#### Scenario: Reading order is recalculated
- **GIVEN** tables have been reclassified as TEXT
- **WHEN** assembling the final page structure
- **THEN** the system SHALL recalculate reading order
- **AND** sort elements by y0 then x0 coordinates
### Requirement: Validation Configuration
The system SHALL provide configurable thresholds for cell validation.
#### Scenario: Default thresholds are applied
- **GIVEN** no custom configuration is provided
- **WHEN** validating tables
- **THEN** the system SHALL use default thresholds:
- max_cell_density: 3.0 cells/10000px²
- min_avg_cell_area: 3000 px²
- min_cell_height: 10 px
#### Scenario: Custom thresholds can be configured
- **GIVEN** custom validation thresholds in configuration
- **WHEN** validating tables
- **THEN** the system SHALL use the custom values
- **AND** apply them consistently to all pages

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