gbanyan
e9357c903b
Phase 5 AI peer review convergence achieved 2026-05-14 with 3/3 reviewers in Accept/Minor band: - Gemini round-2: Accept (splice-ready as-is) - Opus round-2: Minor Revision (N1-N4 → closed in round-4) - codex round-9: Minor Revision (N1/N2 provenance → closed in round-5) Fix-round commits archived:b884d39(round-2),4a6f9c5(round-3),d3ddf74(round-4),128a914(round-5). Reviewer artifacts archived at paper/codex_review_gpt55_v4_round{7,8,9}.md, paper/gemini_review_ v4_round{1,2}.md, paper/opus_review_v4_round{1,2}.md. Phase 6 tasks documented: partner-framing confirmation (reject "statistically insignificant"), manuscript-splice assembly with internal-note strips, DOCX export, partner Jimmy review. Phase 7 tasks documented: iThenticate, IEEE eCF, submission. Lessons added to memory cross-references: codex round-9's DB-verification caught a "majority firm" inference that turned out to be 1:1 ties (round-5 corrected); codex's read-only jitter rerun exposed an unreproducible non-Big-4 range (round-5 replaced with codex-verified range). 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.