gbanyan
9604b273c0
Mechanical copy-edit closing the OPEN/PARTIAL items from paper/codex_review_gpt55_v4_round7.md; substantive empirical content unchanged. Manuscript-splice items (strip internal draft notes, update stale abstract-count note) deferred to final splice. - Phase 4 prose §V-G + §III-K methodology: "candidate classifiers" -> "candidate checks" (closes round-7 m13 + Spot-check 3 wording leak) - Phase 4 prose §II: remove placeholder caveat sentence at the LOOO paragraph (closes round-7 M6 + A4) - References v3: add [42] Stone 1974, [43] Geisser 1975, [44] Vehtari et al. 2017 (44 entries; was 41) — backs the §II LOOO addition - Round-7 review: add row-count clarification note (11 Major / 15 Minor labelled rows vs. the prompt's 9/12 tally) - STATE.md: refresh from stale Phase-2 snapshot to current Phase 5 status — Phases 1-4 complete; codex rounds 1-7 closed at Minor Revision; pending Gemini + Opus rounds + round-2/3 convergence Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.