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gbanyan 52612e14ba Add hybrid signature extraction with name-based verification

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

2025-10-26 23:39:52 +08:00

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PDF Signature Extraction System

Automated extraction of handwritten Chinese signatures from PDF documents using hybrid VLM + Computer Vision approach.

Quick Start

Step 1: Extract Pages from CSV

cd /Volumes/NV2/pdf_recognize
source venv/bin/activate
python extract_pages_from_csv.py

Step 2: Extract Signatures

python extract_signatures_hybrid.py

Documentation

PROJECT_DOCUMENTATION.md - Complete project history, all approaches tested, detailed results
README_page_extraction.md - Page extraction documentation
README_hybrid_extraction.md - Hybrid signature extraction documentation

Current Performance

Test Dataset: 5 PDF pages

Signatures expected: 10
Signatures found: 7
Precision: 100% (no false positives)
Recall: 70%

Key Features

✅ Hybrid Approach: VLM name extraction + CV detection + VLM verification ✅ Name-Based: Signatures saved as signature_周寶蓮.png ✅ No False Positives: Name-specific verification filters out dates, text, stamps ✅ Duplicate Prevention: Only one signature per person ✅ Handles Both: PDFs with/without text layer

File Structure

extract_pages_from_csv.py          # Step 1: Extract pages
extract_signatures_hybrid.py       # Step 2: Extract signatures (CURRENT)
README.md                          # This file
PROJECT_DOCUMENTATION.md           # Complete documentation
README_page_extraction.md          # Page extraction guide
README_hybrid_extraction.md        # Signature extraction guide

Requirements

Python 3.9+
PyMuPDF, OpenCV, NumPy, Requests
Ollama with qwen2.5vl:32b model
Ollama instance: http://192.168.30.36:11434

Data

Input: /Volumes/NV2/PDF-Processing/master_signatures.csv (86,073 rows)
PDFs: /Volumes/NV2/PDF-Processing/total-pdf/batch_*/
Output: /Volumes/NV2/PDF-Processing/signature-image-output/

Status

✅ Page extraction: Tested with 100 files, working ✅ Signature extraction: Tested with 5 files, 70% recall, 100% precision ⏳ Large-scale testing: Pending ⏳ Full dataset (86K files): Pending

See PROJECT_DOCUMENTATION.md for complete details.

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