gbanyan
9b11f03548
Major changes from v2: Terminology: - "digitally replicated" -> "non-hand-signed" throughout (per partner v3 feedback and to avoid implicit accusation) - "Firm A near-universal non-hand-signing" -> "replication-dominated" (per interview nuance: most but not all Firm A partners use replication) Target journal: IEEE TAI -> IEEE Access (per NCKU CSIE list) New methodological sections (III.G-III.L + IV.D-IV.G): - Three convergent threshold methods (KDE antimode + Hartigan dip test / Burgstahler-Dichev McCrary / EM-fitted Beta mixture + logit-GMM robustness check) - Explicit unit-of-analysis discussion (signature vs accountant) - Accountant-level 2D Gaussian mixture (BIC-best K=3 found empirically) - Pixel-identity validation anchor (no manual annotation needed) - Low-similarity negative anchor + Firm A replication-dominated anchor New empirical findings integrated: - Firm A signature cosine UNIMODAL (dip p=0.17) - long left tail = minority hand-signers - Full-sample cosine MULTIMODAL but not cleanly bimodal (BIC prefers 3-comp mixture) - signature-level is continuous quality spectrum - Accountant-level mixture trimodal (C1 Deloitte-heavy 139/141, C2 other Big-4, C3 smaller firms). 2-comp crossings cos=0.945, dh=8.10 - Pixel-identity anchor (310 pairs) gives perfect recall at all cosine thresholds - Firm A anchor rates: cos>0.95=92.5%, dual-rule cos>0.95 AND dh<=8=89.95% New discussion section V.B: "Continuous-quality spectrum vs discrete- behavior regimes" - the core interpretive contribution of v3. References added: Hartigan & Hartigan 1985, Burgstahler & Dichev 1997, McCrary 2008, Dempster-Laird-Rubin 1977, White 1982 (refs 37-41). export_v3.py builds Paper_A_IEEE_Access_Draft_v3.docx (462 KB, +40% vs v2 from expanded methodology + results sections). 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.