gbanyan
6db5d635f5
Codex round 27 returned Minor Revision: 10/11 Major + 14/15 Minor
CLOSED. Two narrow residuals applied:
1. §V-F line 99 'all three candidate classifiers' replaced with
'all three candidate checks' with explicit enumeration
(the inherited box rule, the K=3 hard label, and the
prevalence-calibrated reverse-anchor cut). Keeps the K=3
hard label explicitly descriptive rather than operational.
2. Close-out checklist's stale '~235 words' abstract claim
updated to the verified 243-244 word count.
Deferred to manuscript-assembly time (not blockers for Phase 5
cross-AI peer review):
- §II [42]-[44] citation finalisation (placeholders are
transparent in the current draft state).
- Internal draft notes and close-out checklists (these
explicitly help reviewers track the convergence cycle).
- Manuscript-level lint pass (last step before submission
packaging).
Closure summary across 7 codex rounds (21-27):
- Empirical: ALL Major + Minor findings CLOSED on the
§III/§IV/Phase 4 substantive content.
- Packaging: 2 OPEN items (§II citations, internal notes)
intentionally deferred to manuscript-assembly time.
Phase 5 readiness: substantively YES. The §III v6 + §IV v3.2 +
Phase 4 v2.1 is converged for cross-AI peer review.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
EOF
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.