gbanyan
5e7e76cf35
paper/gemini_review_v3_18_4.md: 68 lines (cleaned from raw output that included CLI 429 retry noise). Gemini broke the codex round-16/17/18 Minor-Revision streak with a Major Revision verdict and four serious findings that 18 prior AI rounds missed: 1. The 656-document exclusion explanation in Section IV-H was a fabricated rationalization contradicting the paper's own cross- document matching methodology. 2. The "two CPAs excluded for disambiguation ties" in Section IV-F.2 was invented; the script has no disambiguation logic. 3. Table XIII (Firm A per-year distribution) was attributed in Appendix B to a script that has no year_month extraction. 4. Inter-CPA negative anchor in script 21_expanded_validation.py drew 50,000 pairs from a LIMIT-3000 random subsample (each signature reused ~33 times), artificially tightening Wilson FAR CIs in Table X. All four verified by independent DB/script inspection before applying fixes. Lesson recorded in user-facing memory: I have a recurrent failure mode of inventing plausible-sounding explanations to fill provenance gaps; future work must verify code/JSON before writing rationale. 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.