gbanyan
ac3372d2d2
#3 Reorder §III around "establish normative baseline → show who deviates": new order A–O with I=Normative Baseline + inter-CPA coincidence floor (old L.0–L.3), J=Firm-Level Deviation (old L.4+L.6), K=Why the distribution gives no threshold (old §I distributional + L.5), L=K=3 partition (old §J), M=Convergent checks (old §K), N=limits (old §M), O=data source (old §N). ~170 cross-refs remapped (two-pass tokenized), incl. 5 spelled-out "Section III-X" refs. #4 Deep trim: §III 10,960→8,461w (−23%). Removed §III↔§IV-M and §III↔§IV-F table duplication (Results keeps canonical tables; §III keeps method+headline+pointer); condensed distributional diagnostics; consolidated repeated caveat. No locked number changed. Also: §V-C header "Case Study"→"Out-of-Sample Target"; abstract 251→250w; housekeeping (rm superseded draft_section_L_bcd.md + v4.0 pandoc docx, remove stale OCR/handoff docs, gitignore .serena/). codex gpt-5.5 review: 0 BLOCKER / 3 MAJOR / 3 MINOR; 3 MAJOR fixed (§III-J.2 observed-vs-counterfactual transition, §III-M table pointers κ→XI/pixel→XIV, §III-N stale tightening figures); 2nd pass clean. Co-Authored-By: Claude Opus 4.8 (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.