gbanyan
338737d9a1
Phase 1.8 follow-up. Validates the v4.0 classifier family against the only hard ground truth in the corpus: pixel_identical_to_closest=1 (byte-identical to nearest same-CPA neighbor; mathematically impossible under independent hand-signing). n = 262 pixel-identical Big-4 signatures. Firm A 145 KPMG 8 PwC 107 EY 2 FAR (lower better; Wilson 95% CI for the misclassification rate): PaperA box rule 0.00% [0.00%, 1.45%] K=3 per-CPA hard label 0.00% [0.00%, 1.45%] Reverse-anchor (calibr.) 0.00% [0.00%, 1.45%] Per-firm: 0% misclass on every firm. Reverse-anchor cut chosen by prevalence calibration (overall replicated rate matches Paper A's 49.58%). Documented v4.0 limitation: no signature-level ground truth for hand-leaning class, so cannot ROC-optimize the cut directly. PwC's 107 pixel-identical signatures despite being the most hand-leaning firm overall (Script 38 per-CPA P_C1=0.31) illustrates the within-firm heterogeneity that v4.0's K=3 mixture captures: a PwC CPA can be hand-leaning on average while still occasionally reusing template signatures. Implication: at the only hard ground truth available in the corpus, all three v4.0 classifiers achieve perfect detection. This satisfies REQ-001 acceptance for pixel-identity FAR. 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.