chore: backup before code cleanup

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

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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egg
2025-12-11 11:55:39 +08:00
parent eff9b0bcd5
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58 changed files with 8226 additions and 175 deletions

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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