gbanyan
66c9194fcf
Fill all 18 placeholders in the condensed v13 submission draft with data verified against the analysis DB and LOCKED canonical scripts; close 12/13 co-author review items (only #8b protocol first-run open). Key changes (need co-author sign-off; see handoff doc): - Firm A out-of-sample HC 0.01% -> 0.42% (buggy 0.0001 from Script 49 same-pair bug, propagated v4.2->v13; never reuse 0.0001) - §III-D empty cell ~=0 -> 7,681 honest reframe (not degenerate crops) - low cosine cut 0.837 -> 0.8547 primary (BCD 2013-2019 closed-world, held-out discipline; 0.8489 confirmed = BCD all-period); HC/MC/HSC unchanged, UN/LH move <=0.4pp Adds Figures 1-5 (real-data plots + schematics), full references, Appendix A/B, UN/HSC ICCR, n-reconciliation, #13 MOPS-metadata survival verification, "參" set-level feasibility probe (negative). Two codex (gpt-5.5) adversarial rounds applied; no fabrication found. Bundle: paper/v13_build/ (markdown source, harvest/figure scripts, figures) for reproducibility. Handoff note for co-author included. Co-Authored-By: Claude Opus 4.8 (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.