gbanyan
980295d5bd
- §IV-D opening: note that the accountant-level dip rejection is fully explained by between-firm composition + integer ties per §III-I.4 (Scripts 39b-e), no longer "the empirical justification for fitting a mixture model" - §IV-E Tables VII/VIII: K=2/K=3 component labels changed from "hand-leaning / mixed / replicated" to position-on-plane labels per §III-J recasting - §IV-I retitled "Inter-CPA Pair-Level Coincidence Rate"; v3.x's "FAR" terminology retroactively reframed; references §IV-M for the v4 Big-4 spike (Script 40b) - New §IV-M (7 tables XIX-XXV): v4-new anchor-based ICCR calibration results consolidated — composition decomposition (Scripts 39b-e), pair-level ICCR sweep (Script 40b), pool- normalised per-signature ICCR (Script 43), document-level ICCR by alarm definition (Script 45), firm-heterogeneity logistic regression + cross-firm hit matrix (Script 44), alert-rate sensitivity (Script 46) - Header bumped to v3.3 (post codex rounds 21-34) Companion to §III v7 commit723a3f6and Phase 4 prose v3 commitb33e20d. 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.