gbanyan
52612e14ba
Implement VLM name extraction + CV detection hybrid approach to
replace unreliable VLM coordinate system with name-based verification.
Key Features:
- VLM extracts signature names (周寶蓮, 魏興海, etc.)
- CV or PDF text layer detects regions
- VLM verifies each region against expected names
- Signatures saved with person names: signature_周寶蓮.png
- Duplicate prevention and rejection handling
Test Results:
- 5 PDF pages tested
- 7/10 signatures extracted (70% recall)
- 100% precision (no false positives)
- No blank regions extracted (previous issue resolved)
Files:
- extract_pages_from_csv.py: Extract pages from CSV (tested: 100 files)
- extract_signatures_hybrid.py: Hybrid extraction (current working solution)
- extract_handwriting.py: CV-only approach (component)
- extract_signatures_vlm.py: Deprecated VLM coordinate approach
- PROJECT_DOCUMENTATION.md: Complete project history and results
- SESSION_INIT.md: Session handoff documentation
- SESSION_CHECKLIST.md: Status checklist
- NEW_SESSION_PROMPT.txt: Template for next session
- HOW_TO_CONTINUE.txt: Visual handoff guide
- COMMIT_SUMMARY.md: Commit preparation guide
- README.md: Quick start guide
- README_page_extraction.md: Page extraction docs
- README_hybrid_extraction.md: Hybrid approach docs
- .gitignore: Exclude diagnostic scripts and outputs
Known Limitations:
- 30% of signatures missed due to conservative CV parameters
- Text layer method untested (all test PDFs are scanned images)
- Performance: ~24 seconds per PDF
Next Steps:
- Tune CV parameters for higher recall
- Test with larger dataset (100+ files)
- Process full dataset (86,073 files)
🤖 Generated with Claude Code
73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
# PDF Signature Extraction System
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Automated extraction of handwritten Chinese signatures from PDF documents using hybrid VLM + Computer Vision approach.
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## Quick Start
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### Step 1: Extract Pages from CSV
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```bash
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cd /Volumes/NV2/pdf_recognize
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source venv/bin/activate
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python extract_pages_from_csv.py
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```
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### Step 2: Extract Signatures
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```bash
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python extract_signatures_hybrid.py
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```
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## Documentation
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- **[PROJECT_DOCUMENTATION.md](PROJECT_DOCUMENTATION.md)** - Complete project history, all approaches tested, detailed results
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- **[README_page_extraction.md](README_page_extraction.md)** - Page extraction documentation
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- **[README_hybrid_extraction.md](README_hybrid_extraction.md)** - Hybrid signature extraction documentation
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## Current Performance
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**Test Dataset:** 5 PDF pages
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- **Signatures expected:** 10
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- **Signatures found:** 7
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- **Precision:** 100% (no false positives)
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- **Recall:** 70%
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## Key Features
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✅ **Hybrid Approach:** VLM name extraction + CV detection + VLM verification
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✅ **Name-Based:** Signatures saved as `signature_周寶蓮.png`
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✅ **No False Positives:** Name-specific verification filters out dates, text, stamps
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✅ **Duplicate Prevention:** Only one signature per person
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✅ **Handles Both:** PDFs with/without text layer
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## File Structure
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```
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extract_pages_from_csv.py # Step 1: Extract pages
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extract_signatures_hybrid.py # Step 2: Extract signatures (CURRENT)
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README.md # This file
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PROJECT_DOCUMENTATION.md # Complete documentation
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README_page_extraction.md # Page extraction guide
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README_hybrid_extraction.md # Signature extraction guide
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```
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## Requirements
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- Python 3.9+
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- PyMuPDF, OpenCV, NumPy, Requests
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- Ollama with qwen2.5vl:32b model
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- Ollama instance: http://192.168.30.36:11434
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## Data
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- **Input:** `/Volumes/NV2/PDF-Processing/master_signatures.csv` (86,073 rows)
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- **PDFs:** `/Volumes/NV2/PDF-Processing/total-pdf/batch_*/`
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- **Output:** `/Volumes/NV2/PDF-Processing/signature-image-output/`
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## Status
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✅ Page extraction: Tested with 100 files, working
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✅ Signature extraction: Tested with 5 files, 70% recall, 100% precision
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⏳ Large-scale testing: Pending
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⏳ Full dataset (86K files): Pending
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See [PROJECT_DOCUMENTATION.md](PROJECT_DOCUMENTATION.md) for complete details.
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