gbanyan
c95c8cb01d
Verdict: Minor Revision (corroborates codex round-7 + Gemini round-1 on disposition) but with explicit dissent on readiness — three Major findings both prior reviewers missed must close before Phase 5 splice. Both-missed Major findings: - M3 (factual overstatement): "98-100% within-source-firm collisions" in Abstract / §I item 6 / §V-C / §V-G / §VI item 4 actually applies only to the stricter same-pair joint event; computed from Table XXIV the deployed any-pair rule yields 98.8 / 76.7 / 83.7 / 77.4 (range 76.7-98.8%). Abstract's "regardless of which Big-4 firm" is wrong as written. - M1 (K=3 mechanism reversion in §IV): Table XVI column headers plus Tables IX/X/XIV/XVII/XVIII still use "hand-leaning / mixed / replicated" mechanism naming that §III-J line 90 explicitly retires; §III/§I/§V/§VI properly use descriptor-position language. - M4 (duplicate heading): Phase 4 prose §V has both "G. Pixel-Identity" (line 105) and "G. Limitations" (line 109); second should be "H". Plus M2 (Gemini-missed): Table-numbering cascade. Renaming XV-B → XIX in isolation collides with §IV-M's existing XIX-XXV; requires cascade XIX→XX, XX→XXI, …, XXV→XXVI. Provenance: 5 fresh spot-checks complementing Gemini's 5; only minor disclosure gap flagged (Script 46 dh=15 plateau ratio derived post-hoc from JSON, not fabrication risk). 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.