cfe65158a3
- Enable PP-StructureV3's use_doc_orientation_classify feature - Detect rotation angle from doc_preprocessor_res.angle - Swap page dimensions (width <-> height) for 90°/270° rotations - Output PDF now correctly displays landscape-scanned content Also includes: - Archive completed openspec proposals - Add simplify-frontend-ocr-config proposal (pending) - Code cleanup and frontend simplification 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
805 lines
28 KiB
Python
805 lines
28 KiB
Python
"""
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Gap Filling Service for OCR Track
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This service detects and fills gaps in PP-StructureV3 output by supplementing
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with Raw OCR text regions when significant content loss is detected.
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The hybrid approach uses Raw OCR's comprehensive text detection to compensate
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for PP-StructureV3's layout model limitations on certain document types.
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"""
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import logging
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from typing import Dict, List, Optional, Tuple, Set, Any
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from dataclasses import dataclass
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from app.models.unified_document import (
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DocumentElement, BoundingBox, ElementType, Dimensions
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)
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from app.core.config import settings
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from app.utils.bbox_utils import normalize_bbox as _normalize_bbox
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logger = logging.getLogger(__name__)
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# Element types that should NOT be supplemented (preserve structural integrity)
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SKIP_ELEMENT_TYPES: Set[ElementType] = {
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ElementType.TABLE,
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ElementType.IMAGE,
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ElementType.FIGURE,
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ElementType.CHART,
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ElementType.DIAGRAM,
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ElementType.HEADER,
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ElementType.FOOTER,
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ElementType.FORMULA,
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ElementType.CODE,
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ElementType.BARCODE,
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ElementType.QR_CODE,
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ElementType.LOGO,
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ElementType.STAMP,
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ElementType.SIGNATURE,
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}
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@dataclass
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class TextRegion:
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"""Represents a raw OCR text region."""
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text: str
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bbox: List[float] # [x0, y0, x1, y1] or polygon format
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confidence: float
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page: int = 0
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@property
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def normalized_bbox(self) -> Tuple[float, float, float, float]:
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"""Get normalized bbox as (x0, y0, x1, y1). Uses shared bbox utility."""
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result = _normalize_bbox(self.bbox)
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return result if result else (0, 0, 0, 0)
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@property
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def center(self) -> Tuple[float, float]:
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"""Get center point of the bbox."""
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x0, y0, x1, y1 = self.normalized_bbox
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return ((x0 + x1) / 2, (y0 + y1) / 2)
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# Element type to IoA threshold mapping
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# TABLE needs strict filtering (low threshold) to prevent duplicate content
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# FIGURE allows more text through (high threshold) to preserve axis labels, legends
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# TEXT/TITLE uses moderate threshold to tolerate boundary detection errors
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ELEMENT_TYPE_IOA_THRESHOLDS = {
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ElementType.TABLE: 'table',
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ElementType.FIGURE: 'figure',
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ElementType.IMAGE: 'figure',
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ElementType.CHART: 'figure',
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ElementType.DIAGRAM: 'figure',
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}
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class GapFillingService:
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"""
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Service for detecting and filling gaps in PP-StructureV3 output.
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This service uses IoA (Intersection over Area) algorithm for coverage detection,
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which correctly measures "small box contained in large box" relationship.
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Key improvements over IoU:
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- IoA = intersection_area / ocr_box_area (non-symmetric)
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- Better for detecting if OCR text is covered by larger layout regions
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- Different thresholds per element type (TEXT, TABLE, FIGURE)
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- Optional boundary shrinking to reduce edge duplicates
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This service:
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1. Calculates coverage of PP-StructureV3 elements over raw OCR regions using IoA
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2. Identifies uncovered raw OCR regions
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3. Supplements uncovered regions as TEXT elements
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4. Deduplicates against existing PP-StructureV3 TEXT elements
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5. Recalculates reading order for the combined result
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"""
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def __init__(
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self,
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coverage_threshold: float = None,
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confidence_threshold: float = None,
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ioa_threshold_text: float = None,
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ioa_threshold_table: float = None,
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ioa_threshold_figure: float = None,
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dedup_ioa_threshold: float = None,
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shrink_pixels: int = None,
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enabled: bool = None
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):
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"""
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Initialize the gap filling service.
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Args:
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coverage_threshold: Coverage ratio below which gap filling activates (default: 0.7)
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confidence_threshold: Minimum confidence for raw OCR regions (default: 0.3)
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ioa_threshold_text: IoA threshold for TEXT/TITLE elements (default: 0.6)
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ioa_threshold_table: IoA threshold for TABLE elements (default: 0.1)
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ioa_threshold_figure: IoA threshold for FIGURE/IMAGE elements (default: 0.8)
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dedup_ioa_threshold: IoA threshold for deduplication (default: 0.5)
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shrink_pixels: Shrink OCR bbox inward by this many pixels (default: 1)
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enabled: Whether gap filling is enabled (default: True)
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"""
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self.coverage_threshold = coverage_threshold if coverage_threshold is not None else getattr(
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settings, 'gap_filling_coverage_threshold', 0.7
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)
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self.confidence_threshold = confidence_threshold if confidence_threshold is not None else getattr(
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settings, 'gap_filling_confidence_threshold', 0.3
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)
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# IoA thresholds per element type
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self.ioa_threshold_text = ioa_threshold_text if ioa_threshold_text is not None else getattr(
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settings, 'gap_filling_ioa_threshold_text', 0.6
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)
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self.ioa_threshold_table = ioa_threshold_table if ioa_threshold_table is not None else getattr(
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settings, 'gap_filling_ioa_threshold_table', 0.1
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)
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self.ioa_threshold_figure = ioa_threshold_figure if ioa_threshold_figure is not None else getattr(
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settings, 'gap_filling_ioa_threshold_figure', 0.8
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)
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self.dedup_ioa_threshold = dedup_ioa_threshold if dedup_ioa_threshold is not None else getattr(
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settings, 'gap_filling_dedup_ioa_threshold', 0.5
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)
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# Boundary shrinking
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self.shrink_pixels = shrink_pixels if shrink_pixels is not None else getattr(
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settings, 'gap_filling_shrink_pixels', 1
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)
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self.enabled = enabled if enabled is not None else getattr(
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settings, 'gap_filling_enabled', True
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)
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def should_activate(
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self,
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raw_ocr_regions: List[TextRegion],
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pp_structure_elements: List[DocumentElement]
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) -> Tuple[bool, float]:
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"""
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Determine if gap filling should be activated.
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Gap filling activates when:
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1. Coverage ratio is below threshold (default: 70%)
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2. OR element count disparity is significant
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Args:
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raw_ocr_regions: List of raw OCR text regions
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pp_structure_elements: List of PP-StructureV3 elements
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Returns:
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Tuple of (should_activate, coverage_ratio)
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"""
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if not self.enabled:
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return False, 1.0
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if not raw_ocr_regions:
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return False, 1.0
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# Calculate coverage
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covered_count = 0
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for region in raw_ocr_regions:
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if self._is_region_covered(region, pp_structure_elements):
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covered_count += 1
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coverage_ratio = covered_count / len(raw_ocr_regions)
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# Check activation conditions
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should_activate = coverage_ratio < self.coverage_threshold
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if should_activate:
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logger.info(
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f"Gap filling activated: coverage={coverage_ratio:.2%} < threshold={self.coverage_threshold:.0%}, "
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f"raw_regions={len(raw_ocr_regions)}, pp_elements={len(pp_structure_elements)}"
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)
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else:
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logger.debug(
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f"Gap filling not needed: coverage={coverage_ratio:.2%} >= threshold={self.coverage_threshold:.0%}"
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)
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return should_activate, coverage_ratio
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def find_uncovered_regions(
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self,
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raw_ocr_regions: List[TextRegion],
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pp_structure_elements: List[DocumentElement]
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) -> List[TextRegion]:
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"""
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Find raw OCR regions not covered by PP-StructureV3 elements.
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A region is considered covered if:
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1. Its center point falls inside any PP-StructureV3 element bbox, OR
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2. IoU with any PP-StructureV3 element exceeds iou_threshold
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Args:
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raw_ocr_regions: List of raw OCR text regions
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pp_structure_elements: List of PP-StructureV3 elements
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Returns:
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List of uncovered raw OCR regions
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"""
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uncovered = []
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for region in raw_ocr_regions:
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# Skip low confidence regions
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if region.confidence < self.confidence_threshold:
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continue
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if not self._is_region_covered(region, pp_structure_elements):
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uncovered.append(region)
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logger.debug(f"Found {len(uncovered)} uncovered regions out of {len(raw_ocr_regions)}")
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return uncovered
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def _get_ioa_threshold_for_element(self, element_type: ElementType) -> float:
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"""
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Get the IoA threshold for a specific element type.
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Different element types have different thresholds:
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- TABLE: 0.1 (strict, prevents duplicate table content)
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- FIGURE/IMAGE: 0.8 (preserves text inside figures)
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- TEXT/others: 0.6 (tolerates boundary errors)
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Args:
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element_type: The element type to get threshold for
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Returns:
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IoA threshold value
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"""
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threshold_type = ELEMENT_TYPE_IOA_THRESHOLDS.get(element_type, 'text')
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if threshold_type == 'table':
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return self.ioa_threshold_table
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elif threshold_type == 'figure':
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return self.ioa_threshold_figure
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else:
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return self.ioa_threshold_text
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def _shrink_bbox(
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self,
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bbox: Tuple[float, float, float, float],
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pixels: int
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) -> Tuple[float, float, float, float]:
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"""
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Shrink a bounding box inward by the specified number of pixels.
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This reduces false "uncovered" detection at region boundaries.
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Args:
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bbox: Original bbox (x0, y0, x1, y1)
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pixels: Number of pixels to shrink on each side
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Returns:
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Shrunk bbox (x0, y0, x1, y1)
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"""
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x0, y0, x1, y1 = bbox
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# Ensure we don't shrink to negative width/height
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width = x1 - x0
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height = y1 - y0
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max_shrink = min(width / 2, height / 2, pixels)
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return (
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x0 + max_shrink,
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y0 + max_shrink,
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x1 - max_shrink,
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y1 - max_shrink
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)
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def _is_region_covered(
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self,
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region: TextRegion,
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pp_structure_elements: List[DocumentElement],
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skip_table_coverage: bool = False
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) -> bool:
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"""
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Check if a raw OCR region is covered by any PP-StructureV3 element.
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Uses IoA (Intersection over Area) instead of IoU for better coverage detection.
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IoA = intersection_area / ocr_box_area
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This correctly measures "OCR box is contained in layout region".
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Different element types use different IoA thresholds:
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- TABLE: 0.1 (strict, any overlap means covered)
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- FIGURE/IMAGE: 0.8 (preserve text inside figures like axis labels)
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- TEXT/others: 0.6 (tolerate boundary errors)
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Args:
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region: Raw OCR text region
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pp_structure_elements: List of PP-StructureV3 elements
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skip_table_coverage: If True, don't consider TABLE elements as covering.
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Default is False - TABLE elements DO cover regions
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to prevent duplicate rendering of table cell content.
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Returns:
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True if the region is covered
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"""
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center_x, center_y = region.center
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region_bbox = region.normalized_bbox
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# Apply boundary shrinking to reduce edge duplicates
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if self.shrink_pixels > 0:
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region_bbox = self._shrink_bbox(region_bbox, self.shrink_pixels)
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for element in pp_structure_elements:
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# Check TABLE elements for coverage (default behavior)
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# This prevents gap_fill from adding duplicate text inside table areas
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if skip_table_coverage and element.type == ElementType.TABLE:
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continue
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elem_bbox = (
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element.bbox.x0, element.bbox.y0,
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element.bbox.x1, element.bbox.y1
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)
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# Check 1: Center point falls inside element bbox
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if self._point_in_bbox(center_x, center_y, elem_bbox):
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return True
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# Check 2: IoA exceeds element-type-specific threshold
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# IoA = intersection_area / ocr_box_area
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ioa = self._calculate_ioa(region_bbox, elem_bbox)
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threshold = self._get_ioa_threshold_for_element(element.type)
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if ioa > threshold:
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return True
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return False
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def deduplicate_regions(
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self,
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uncovered_regions: List[TextRegion],
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pp_structure_elements: List[DocumentElement]
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) -> List[TextRegion]:
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"""
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Remove regions that highly overlap with existing PP-StructureV3 TEXT elements.
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Uses IoA (Intersection over Area) for deduplication to correctly detect
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when an OCR region is already covered by an existing TEXT element.
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Args:
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uncovered_regions: List of uncovered raw OCR regions
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pp_structure_elements: List of PP-StructureV3 elements
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Returns:
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Deduplicated list of regions
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"""
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# Get TEXT elements only for deduplication
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text_elements = [
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e for e in pp_structure_elements
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if e.type not in SKIP_ELEMENT_TYPES
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]
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deduplicated = []
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for region in uncovered_regions:
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region_bbox = region.normalized_bbox
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# Apply boundary shrinking for deduplication as well
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if self.shrink_pixels > 0:
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region_bbox = self._shrink_bbox(region_bbox, self.shrink_pixels)
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is_duplicate = False
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for element in text_elements:
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elem_bbox = (
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element.bbox.x0, element.bbox.y0,
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element.bbox.x1, element.bbox.y1
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)
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# Use IoA for deduplication
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ioa = self._calculate_ioa(region_bbox, elem_bbox)
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if ioa > self.dedup_ioa_threshold:
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logger.debug(
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f"Skipping duplicate region (IoA={ioa:.2f}): '{region.text[:30]}...'"
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)
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is_duplicate = True
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break
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if not is_duplicate:
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deduplicated.append(region)
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removed_count = len(uncovered_regions) - len(deduplicated)
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if removed_count > 0:
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logger.debug(f"Removed {removed_count} duplicate regions")
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return deduplicated
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def convert_regions_to_elements(
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self,
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regions: List[TextRegion],
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page_number: int,
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start_element_id: int = 0
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) -> List[DocumentElement]:
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"""
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Convert raw OCR regions to DocumentElement objects.
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Args:
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regions: List of raw OCR regions to convert
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page_number: Page number for the elements
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start_element_id: Starting ID counter for elements
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Returns:
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List of DocumentElement objects
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"""
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elements = []
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for idx, region in enumerate(regions):
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x0, y0, x1, y1 = region.normalized_bbox
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element = DocumentElement(
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element_id=f"gap_fill_{page_number}_{start_element_id + idx}",
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type=ElementType.TEXT,
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content=region.text,
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bbox=BoundingBox(x0=x0, y0=y0, x1=x1, y1=y1),
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confidence=region.confidence,
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metadata={
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'source': 'gap_filling',
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'original_confidence': region.confidence
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}
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)
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elements.append(element)
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return elements
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def recalculate_reading_order(
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self,
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elements: List[DocumentElement]
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) -> List[int]:
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"""
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Recalculate reading order for elements based on position.
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Sorts elements by y0 (top to bottom) then x0 (left to right).
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Args:
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elements: List of DocumentElement objects
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Returns:
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List of element indices in reading order
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"""
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# Create indexed list with position info
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indexed_elements = [
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(idx, e.bbox.y0, e.bbox.x0)
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for idx, e in enumerate(elements)
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]
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# Sort by y0 then x0
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indexed_elements.sort(key=lambda x: (x[1], x[2]))
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# Return indices in reading order
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return [idx for idx, _, _ in indexed_elements]
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def merge_adjacent_regions(
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self,
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regions: List[TextRegion],
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max_horizontal_gap: float = 20.0,
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max_vertical_gap: float = 5.0
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) -> List[TextRegion]:
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"""
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Merge fragmented adjacent regions on the same line.
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This is optional and can reduce fragmentation from raw OCR.
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Args:
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regions: List of raw OCR regions
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max_horizontal_gap: Maximum horizontal gap to merge (pixels)
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max_vertical_gap: Maximum vertical gap to merge (pixels)
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Returns:
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List of merged regions
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"""
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if not regions:
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return regions
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# Sort by y0, then x0
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|
sorted_regions = sorted(
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regions,
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key=lambda r: (r.normalized_bbox[1], r.normalized_bbox[0])
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)
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merged = []
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current = sorted_regions[0]
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for next_region in sorted_regions[1:]:
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curr_bbox = current.normalized_bbox
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next_bbox = next_region.normalized_bbox
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# Check if on same line (vertical overlap)
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curr_y_center = (curr_bbox[1] + curr_bbox[3]) / 2
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next_y_center = (next_bbox[1] + next_bbox[3]) / 2
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vertical_distance = abs(curr_y_center - next_y_center)
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# Check horizontal gap
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horizontal_gap = next_bbox[0] - curr_bbox[2]
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if (vertical_distance < max_vertical_gap and
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0 <= horizontal_gap <= max_horizontal_gap):
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# Merge regions
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merged_bbox = [
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min(curr_bbox[0], next_bbox[0]),
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min(curr_bbox[1], next_bbox[1]),
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max(curr_bbox[2], next_bbox[2]),
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max(curr_bbox[3], next_bbox[3])
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]
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current = TextRegion(
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text=current.text + " " + next_region.text,
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bbox=merged_bbox,
|
|
confidence=min(current.confidence, next_region.confidence),
|
|
page=current.page
|
|
)
|
|
else:
|
|
merged.append(current)
|
|
current = next_region
|
|
|
|
merged.append(current)
|
|
|
|
if len(merged) < len(regions):
|
|
logger.debug(f"Merged {len(regions)} regions into {len(merged)}")
|
|
|
|
return merged
|
|
|
|
def fill_gaps(
|
|
self,
|
|
raw_ocr_regions: List[Dict[str, Any]],
|
|
pp_structure_elements: List[DocumentElement],
|
|
page_number: int,
|
|
ocr_dimensions: Optional[Dict[str, Any]] = None,
|
|
pp_dimensions: Optional[Dimensions] = None
|
|
) -> Tuple[List[DocumentElement], Dict[str, Any]]:
|
|
"""
|
|
Main entry point: detect gaps and fill with raw OCR regions.
|
|
|
|
Args:
|
|
raw_ocr_regions: Raw OCR results (list of dicts with text, bbox, confidence)
|
|
pp_structure_elements: PP-StructureV3 elements
|
|
page_number: Current page number
|
|
ocr_dimensions: OCR image dimensions for coordinate alignment
|
|
pp_dimensions: PP-Structure dimensions for coordinate alignment
|
|
|
|
Returns:
|
|
Tuple of (supplemented_elements, statistics)
|
|
"""
|
|
statistics = {
|
|
'enabled': self.enabled,
|
|
'activated': False,
|
|
'coverage_ratio': 1.0,
|
|
'raw_ocr_count': len(raw_ocr_regions),
|
|
'pp_structure_count': len(pp_structure_elements),
|
|
'uncovered_count': 0,
|
|
'deduplicated_count': 0,
|
|
'supplemented_count': 0
|
|
}
|
|
|
|
if not self.enabled:
|
|
logger.debug("Gap filling is disabled")
|
|
return [], statistics
|
|
|
|
# Convert raw OCR regions to TextRegion objects
|
|
text_regions = self._convert_raw_ocr_regions(
|
|
raw_ocr_regions, page_number, ocr_dimensions, pp_dimensions
|
|
)
|
|
|
|
if not text_regions:
|
|
logger.debug("No valid text regions to process")
|
|
return [], statistics
|
|
|
|
# Check if gap filling should activate
|
|
should_activate, coverage_ratio = self.should_activate(
|
|
text_regions, pp_structure_elements
|
|
)
|
|
statistics['coverage_ratio'] = coverage_ratio
|
|
statistics['activated'] = should_activate
|
|
|
|
if not should_activate:
|
|
return [], statistics
|
|
|
|
# Find uncovered regions
|
|
uncovered = self.find_uncovered_regions(text_regions, pp_structure_elements)
|
|
statistics['uncovered_count'] = len(uncovered)
|
|
|
|
if not uncovered:
|
|
logger.debug("No uncovered regions found")
|
|
return [], statistics
|
|
|
|
# Deduplicate against existing TEXT elements
|
|
deduplicated = self.deduplicate_regions(uncovered, pp_structure_elements)
|
|
statistics['deduplicated_count'] = len(deduplicated)
|
|
|
|
if not deduplicated:
|
|
logger.debug("All uncovered regions were duplicates")
|
|
return [], statistics
|
|
|
|
# Optional: Merge adjacent regions
|
|
# merged = self.merge_adjacent_regions(deduplicated)
|
|
|
|
# Convert to DocumentElements
|
|
start_id = len(pp_structure_elements)
|
|
supplemented = self.convert_regions_to_elements(
|
|
deduplicated, page_number, start_id
|
|
)
|
|
statistics['supplemented_count'] = len(supplemented)
|
|
|
|
logger.info(
|
|
f"Gap filling complete: supplemented {len(supplemented)} elements "
|
|
f"(coverage: {coverage_ratio:.2%} -> estimated {(coverage_ratio + len(supplemented)/len(text_regions) if text_regions else 0):.2%})"
|
|
)
|
|
|
|
return supplemented, statistics
|
|
|
|
def _convert_raw_ocr_regions(
|
|
self,
|
|
raw_regions: List[Dict[str, Any]],
|
|
page_number: int,
|
|
ocr_dimensions: Optional[Dict[str, Any]] = None,
|
|
pp_dimensions: Optional[Dimensions] = None
|
|
) -> List[TextRegion]:
|
|
"""
|
|
Convert raw OCR region dicts to TextRegion objects.
|
|
|
|
Handles coordinate alignment if dimensions are provided.
|
|
|
|
Args:
|
|
raw_regions: List of raw OCR region dictionaries
|
|
page_number: Current page number
|
|
ocr_dimensions: OCR image dimensions
|
|
pp_dimensions: PP-Structure dimensions
|
|
|
|
Returns:
|
|
List of TextRegion objects
|
|
"""
|
|
text_regions = []
|
|
|
|
# Calculate scale factors if needed
|
|
scale_x, scale_y = 1.0, 1.0
|
|
if ocr_dimensions and pp_dimensions:
|
|
ocr_width = ocr_dimensions.get('width', 0)
|
|
ocr_height = ocr_dimensions.get('height', 0)
|
|
|
|
if ocr_width > 0 and pp_dimensions.width > 0:
|
|
scale_x = pp_dimensions.width / ocr_width
|
|
if ocr_height > 0 and pp_dimensions.height > 0:
|
|
scale_y = pp_dimensions.height / ocr_height
|
|
|
|
if scale_x != 1.0 or scale_y != 1.0:
|
|
logger.debug(f"Coordinate scaling: x={scale_x:.3f}, y={scale_y:.3f}")
|
|
|
|
for region in raw_regions:
|
|
text = region.get('text', '')
|
|
if not text or not text.strip():
|
|
continue
|
|
|
|
confidence = region.get('confidence', 0.0)
|
|
bbox_raw = region.get('bbox', [])
|
|
|
|
# Normalize bbox
|
|
if isinstance(bbox_raw, dict):
|
|
# Dict format: {x_min, y_min, x_max, y_max}
|
|
bbox = [
|
|
bbox_raw.get('x_min', 0),
|
|
bbox_raw.get('y_min', 0),
|
|
bbox_raw.get('x_max', 0),
|
|
bbox_raw.get('y_max', 0)
|
|
]
|
|
elif isinstance(bbox_raw, (list, tuple)):
|
|
bbox = list(bbox_raw)
|
|
else:
|
|
continue
|
|
|
|
# Apply scaling if needed
|
|
if scale_x != 1.0 or scale_y != 1.0:
|
|
# Check if nested list format [[x1,y1], [x2,y2], ...]
|
|
if len(bbox) >= 1 and isinstance(bbox[0], (list, tuple)):
|
|
bbox = [
|
|
[pt[0] * scale_x, pt[1] * scale_y]
|
|
for pt in bbox if len(pt) >= 2
|
|
]
|
|
elif len(bbox) == 4 and not isinstance(bbox[0], (list, tuple)):
|
|
# Simple [x0, y0, x1, y1] format
|
|
bbox = [
|
|
bbox[0] * scale_x, bbox[1] * scale_y,
|
|
bbox[2] * scale_x, bbox[3] * scale_y
|
|
]
|
|
elif len(bbox) >= 8:
|
|
# Flat polygon format [x1, y1, x2, y2, ...]
|
|
bbox = [
|
|
bbox[i] * (scale_x if i % 2 == 0 else scale_y)
|
|
for i in range(len(bbox))
|
|
]
|
|
|
|
text_regions.append(TextRegion(
|
|
text=text,
|
|
bbox=bbox,
|
|
confidence=confidence,
|
|
page=page_number
|
|
))
|
|
|
|
return text_regions
|
|
|
|
@staticmethod
|
|
def _point_in_bbox(
|
|
x: float, y: float,
|
|
bbox: Tuple[float, float, float, float]
|
|
) -> bool:
|
|
"""Check if point (x, y) is inside bbox (x0, y0, x1, y1)."""
|
|
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],
|
|
bbox2: Tuple[float, float, float, float]
|
|
) -> float:
|
|
"""
|
|
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)
|
|
|
|
Returns:
|
|
IoU value between 0 and 1
|
|
"""
|
|
# Calculate intersection
|
|
x0 = max(bbox1[0], bbox2[0])
|
|
y0 = max(bbox1[1], bbox2[1])
|
|
x1 = min(bbox1[2], bbox2[2])
|
|
y1 = min(bbox1[3], bbox2[3])
|
|
|
|
if x1 <= x0 or y1 <= y0:
|
|
return 0.0
|
|
|
|
intersection = (x1 - x0) * (y1 - y0)
|
|
|
|
# Calculate union
|
|
area1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
|
|
area2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
|
|
union = area1 + area2 - intersection
|
|
|
|
if union <= 0:
|
|
return 0.0
|
|
|
|
return intersection / union
|