gbanyan
e33c538162
Verdict: Minor Revision (corroborates codex round-7). Convergence with codex: all 4 spot-checked round-26 Major findings confirmed CLOSED in current drafts; all 5 numerical provenance spot-checks VERIFIED against named scripts (Spearman 0.879 / S38; Firm A doc 0.62 / S45; byte-identical 145/8/107/2 / S40; dip p_median=0.35 / S39e; logistic OR 0.053/0.010/0.027 / S44). Net-new findings beyond codex round-7: - Empirical blocker: partner's "statistically insignificant" framing of firm heterogeneity (raised 2026-05-13) is explicitly unsupported — OR of 0.053/0.010/0.027 means 19x-100x lower odds for B/C/D vs Firm A even after pool-size control. Gemini recommends explicit rejection in any partner-side response. - Net-new minor: §IV "Table XV-B" should be renumbered to "Table XIX" for IEEE Access sequential-integer style. - Net-new minor: Table XV (150,442 descriptor-complete) and §III-L.2 ICCR analyses (150,453 vector-complete) need a footnote pointing back to §III-G's sample-size reconciliation. 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.