gbanyan
9392f30aef
Light §IV-K secondary analysis per v4.0 author choice (codex
round-22 open question 1). Reruns the K=3 mixture + Paper A
operational-rule per-CPA hand_frac on the full accountant dataset
(n = 686) and compares to the Big-4 primary scope (n = 437).
Results:
Component drift Big-4 -> Full:
C1 hand-leaning |dcos| = 0.018, |ddh| = 2.0, |dwt| = 0.14
C2 mixed |dcos| = 0.002, |ddh| = 0.3, |dwt| = 0.02
C3 replicated |dcos| = 0.000, |ddh| = 0.0, |dwt| = 0.12
Spearman rho (P_C1 vs paperA_hand_frac):
Big-4: +0.9627
Full dataset: +0.9558
|drift| = 0.0069
Reading: K=3 component ordering and Spearman convergence are
preserved at full scope, supporting the v4.0 reproducibility
claim. Component locations and weights shift modestly because
mid/small-firm composition broadens C1 (hand-leaning) and reduces
C3 weight; this is expected since mid/small firms include
hand-leaning CPAs that the Big-4-primary scope deliberately
excludes. Crossings and component locations are NOT operationally
interchangeable between scopes; §IV-K reports them only as a
robustness cross-check.
The five-way moderate-confidence band is NOT re-evaluated here
(Light scope); §IV-J flags it as inherited from v3.x calibration
without v4-specific recalibration.
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.