gbanyan
da455791de
Address all 29 items from the fused reviewer report (Gemini 3.1 Pro + ChatGPT 5.5 + Opus 4.8): 3 fatal, 4 severe, arbitration A/B, 5 fusion-new, 15 minor. All new numbers computed from signature_analysis.db; nothing fabricated. Claim honesty (F1/F3/F4/F7/G3): - Retract all "139x the floor" comparisons; ICCR -> between-accountant specificity proxy throughout; state within-accountant FPR is not estimable and ICCR is not even a bound (anti-conservative direction). - Firm A reframed as quasi-positive known-positive benchmark (not blinded). - byte-identity recast as prevalence signal, not a recall/sanity check. - tunable -> single-direction conservativeness dial (no P-R frontier). New data analysis (verified, bit-reproducible via committed scripts): - F2/G1 (Sec V-B): 880-PDF imaging-pipeline audit (Table V) - plain scans 82% (2013) -> 1% (2021); producer strings name scanner hardware (Fuji Xerox D125 etc.); substrate transforms at 2020/21 = named confound. - F5 (Sec IV-C): four robustness checks - pool-size stratification, accountant-clustered bootstrap (gap 53.7pp [49.5,57.5]), firm+year FE logistic (B/C/D OR 0.06-0.12), leave-one-year-out (gap 53.1-54.9pp). - byte-identity era split: 30 scan-era (18 Firm A, pipeline-robust) vs 232 digital-era (detectability-inflated, hedged). - G5: archive-wide 888 expected chance HC flags [677,1098]. - M4: Figure 3 replaced with real 2D density (n=150,441). Structure/minor: abstract restructured (M1); operational definition (M2); interview disclaimer (M3); Threats to Validity subsection (M8); review protocol framed as design not evidence (M9); N reconciliations (M10/M11); Table II-c 2020-23 five-way (M12); Section refs, American spelling, notation table (M5/M13/M15); reference URLs verified (M14). Open (author-only): placeholders (M13), II-b/IV table merge (M15). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qn59FdF9JMyfFg3sjcUNNG
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.