gbanyan
4cf21a64b2
Spike checkpoint addressing codex round-31 review of Script 43:
- 44 (firm-matched-pool regression): logistic hit ~ firm + log(pool_size)
refutes the "Firm A excess is pool-size confound" reviewer attack.
After controlling for log(pool_size), Firm B/C/D ORs are 0.053 /
0.010 / 0.027 vs Firm A reference (z = 62 / 60 / 42 sigma). Cross-
firm hit matrix shows 98-100% of any-pair hits have candidates
from the SAME firm (different CPA), confirming within-firm cross-
CPA template sharing as the dominant collision mechanism.
- 45 (full 5-way doc FAR): per-signature and per-document FAR for
three alarm definitions (HC / HC+MC / HC+MC+HSC). Per-document
HC alarm FAR=17.97%, HC+MC alarm FAR=33.75% (operational rule),
per-firm doc FAR for Firm A 62%, B/C/D 9-16%.
Together these resolve codex round-31's three main concerns:
firm/pool confound, documentation completeness on MC band, and
the operational specificity ceiling. Companion artefacts in
reports/v4_big4/{firm_matched_pool, doc_level_far_full}/.
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.