gbanyan
1dfbc5f000
Gemini 3.1 Pro round-15 full-paper review of v3.14 returned Accept with four MINOR polish suggestions. All four applied in this commit. 1. Table XIII column header: "mean cosine" renamed to "mean best-match cosine" to match the underlying metric (per- signature best-match over the full same-CPA pool) and prevent readers from inferring a simpler per-year statistic. 2. Methodology III-L (L284): added a forward-pointer in the first threshold-convention note to Section IV-G.3, explicitly confirming that replacing the 0.95 round-number heuristic with the nearby accountant-level 2D-GMM marginal crossing 0.945 alters aggregate firm-level capture rates by at most ~1.2 percentage points. This pre-empts a reader who might worry about the methodological tension between the heuristic and the mixture-derived convergence band. 3. Results IV-I document-level aggregation (L383): "Document-level rates therefore bound the share..." rewritten as "represent the share..." Gemini correctly noted that worst-case aggregation directly assigns (subject to classifier error), so "bound" spuriously implies an inequality not actually present. 4. Results IV-G.4 Sanity Sample (L273): "inter-rater agreement with the classifier" rewritten as "full human--classifier agreement (30/30)". Inter-rater conventionally refers to human-vs-human agreement; human-vs-classifier is the correct term here. No substantive changes; no tables recomputed. Gemini round-15 verdict was Accept with these four items framed as nice-to-have rather than blockers; applying them brings v3.15 to a fully polished state before manual DOCX packaging. 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.