Based on pp_demo analysis, PPStructureV3 returns table_res_list containing
cell_box_list which was previously ignored. This commit:
- Extract table_res_list from PPStructureV3 result alongside parsing_res_list
- Add table_res_list parameter to _process_parsing_res_list()
- Prioritize cell_box_list from table_res_list over SLANeXt extraction
- Match tables by HTML content or use first available
Priority order for cell boxes:
1. table_res_list.cell_box_list (native, already absolute coords)
2. res_data['boxes'] (unlikely in PaddleX 3.x)
3. Direct SLANeXt model call (fallback)
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Co-Authored-By: Claude <noreply@anthropic.com>
Phase 1-3 implementation of extract-table-cell-boxes proposal:
- Add enable_table_cell_boxes_extraction config option
- Implement lazy-loaded SLANeXt model caching in PPStructureEnhanced
- Add _extract_cell_boxes_with_slanet() method for direct model invocation
- Supplement PPStructureV3 table processing with SLANeXt cell boxes
- Add _compute_table_grid_from_cell_boxes() for column width calculation
- Modify draw_table_region() to use cell_boxes for accurate layout
Key features:
- Auto-detect table type (wired/wireless) using PP-LCNet classifier
- Convert 8-point polygon bbox to 4-point rectangle
- Graceful fallback to equal distribution when cell_boxes unavailable
- Proper coordinate transformation with scaling support
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Co-Authored-By: Claude <noreply@anthropic.com>
- Archive unify-image-scaling proposal to archive/2025-11-28
- Create new extract-table-cell-boxes proposal for supplementing PPStructureV3
with direct SLANeXt model calls to extract table cell bounding boxes
- Add debug logging to pp_structure_enhanced.py for table cell boxes investigation
- Discovered that PPStructureV3 high-level API filters out cell bbox data,
but paddlex.create_model() can directly invoke underlying models
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Co-Authored-By: Claude <noreply@anthropic.com>
Table data format fixes (ocr_to_unified_converter.py):
- Fix ElementType string conversion using value-based lookup
- Add content-based HTML table detection (reclassify TEXT to TABLE)
- Use BeautifulSoup for robust HTML table parsing
- Generate TableData with fully populated cells arrays
Image cropping for OCR track (pp_structure_enhanced.py):
- Add _crop_and_save_image method for extracting image regions
- Pass source_image_path to _process_parsing_res_list
- Return relative filename (not full path) for saved_path
- Consistent with Direct Track image saving pattern
Also includes:
- Add beautifulsoup4 to requirements.txt
- Add architecture overview documentation
- Archive fix-ocr-track-table-data-format proposal (22/24 tasks)
Known issues: OCR track images are restored but still have quality issues
that will be addressed in a follow-up proposal.
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Co-Authored-By: Claude <noreply@anthropic.com>
Multi-line text rendering (pdf_generator_service.py):
- Calculate font size by dividing bbox height by number of lines
- Start Y coordinate from bbox TOP instead of bottom
- Use non_empty_lines for proper line positioning
HTML table detection:
- pp_structure_enhanced.py: Detect HTML tables in 'text' type content
and reclassify to TABLE when <table tag found
- pdf_generator_service.py: Content-based reclassification from TEXT
to TABLE during UnifiedDocument parsing
- ocr_to_unified_converter.py: Fallback to check 'content' field for
HTML tables when 'html' field is empty
Known issue: OCR processing still has quality issues that need further
investigation and fixes.
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Co-Authored-By: Claude <noreply@anthropic.com>
- Fix parsing_res_list field mapping (block_label, block_content, block_bbox)
- Add fine-grained PP-StructureV3 configuration parameters
- Lower detection thresholds (0.5→0.2) for more sensitive element detection
- Use 'small' merge mode instead of default to minimize bbox merging
- Add layout_nms, unclip_ratio, text_det thresholds for better control
- Result: Doubled element detection from 6 to 12 elements on complex diagrams
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Co-Authored-By: Claude <noreply@anthropic.com>
Implement critical fixes for image and table rendering in PDF generation.
**Image Handling Fixes**:
- Implemented _save_image() in pp_structure_enhanced.py
- Creates imgs/ subdirectory for saved images
- Handles both file paths and numpy arrays
- Returns relative path for reference
- Adds proper error handling and logging
- Added saved_path field to image elements for path tracking
- Created _get_image_path() helper with fallback logic
- Checks saved_path, path, image_path in content
- Falls back to metadata fields
- Logs warnings for missing paths
**Table Rendering Fixes**:
- Fixed table rendering to use element's own bbox directly
- No longer depends on fake table_*.png references
- Supports both bbox and bbox_polygon formats
- Inline conversion for different bbox formats
- Maintains backward compatibility with legacy approach
- Improved error handling for missing bbox data
**Status**:
- Phase 1 tasks 1.1 and 1.2: ✅ Completed
- Phase 1 tasks 2.1, 2.2, and 2.3: ✅ Completed
- Testing pending due to backend availability
These fixes resolve the critical issues where images never appeared
and tables never rendered in generated PDFs.
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Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
- Backend failed to start with ModuleNotFoundError for torch module
- torch was imported as hard dependency but not in requirements.txt
- Project uses PaddlePaddle which has its own CUDA implementation
Changes:
- Make torch import optional with try/except in ocr_service.py
- Make torch import optional in pp_structure_enhanced.py
- Add cleanup_gpu_memory() method using PaddlePaddle's memory management
- Add check_gpu_memory() method to monitor available GPU memory
- Use paddle.device.cuda.empty_cache() for GPU cleanup
- Use torch.cuda only if TORCH_AVAILABLE flag is True
- Add cleanup calls after OCR processing to prevent OOM errors
- Add memory checks before GPU-intensive operations
Benefits:
- Backend can start without torch installed
- GPU memory is properly managed using PaddlePaddle
- Optional torch support provides additional memory monitoring
- Prevents GPU OOM errors during document processing
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Co-Authored-By: Claude <noreply@anthropic.com>
Implements the converter that transforms PP-StructureV3 OCR results into
the UnifiedDocument format, enabling consistent output for both OCR and
direct extraction tracks.
- Create OCRToUnifiedConverter class with full element type mapping
- Handle both enhanced (parsing_res_list) and standard markdown results
- Support 4-point and simple bbox formats for coordinates
- Establish element relationships (captions, lists, headers)
- Integrate converter into OCR service dual-track processing
- Update tasks.md marking section 3.3 complete
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