gbanyan
8ac09888ae
Follow-up to Script 32 verdict C. Tests whether using the non-Firm-A
population (515 CPAs) as a "fully-replicated reference" recovers the
Paper A hand-signed signal through deviation analysis on Firm A.
Methodology:
* Robust 2D Gaussian fit (MCD, support_fraction=0.85) on
(cos_mean, dh_mean) of all_non_A CPAs. Reference center =
(cos=0.946, dh=8.29).
* Score Firm A CPAs by symmetric Mahalanobis distance, log-
likelihood, and directional cosine left-tail percentile.
* Cross-validate against Paper A's per-CPA hand_frac proxy
(signatures with cos<=0.95 OR dh>5).
Key findings:
* Directional metric (-cos_left_tail_pct) vs Paper A hand_frac:
Spearman rho = +0.744 (p < 1e-30) -- PAPER_C_STRONG.
* Symmetric Mahalanobis vs hand_frac: rho = -0.927 (p < 1e-73).
The negative sign is a feature, not a bug: Firm A bifurcates
into two anomaly directions from the non-Firm-A reference --
(a) ultra-replicated CPAs (cos>=0.985, dh~1) sitting beyond
the reference's high-cos tail, and (b) hand-signed CPAs
(cos~0.95, dh~6-7) sitting near or below the reference
center. Symmetric distance lumps both into a positive
magnitude; directional metrics distinguish them.
Implication: a "Paper C" reframing is statistically supported.
Use non-Firm-A as the replication reference, not Firm A as the
hand-signed anchor. This removes the "why is Firm A ground
truth?" reviewer attack and reveals the bifurcation structure
that Paper A's symmetric framing obscures.
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.