gbanyan
51d15b32a5
Partner v4 (signature_paper_draft_v4) proposed 3 substantive improvements; partner confirmed the 2013-2019 restriction was an error (sample stays 2013-2023). The remaining suggestions are adopted with our own data. ## New scripts - Script 22 (partner ranking): ranks all Big-4 auditor-years by mean max-cosine. Firm A occupies 95.9% of top-10% (base 27.8%), 3.5x concentration ratio. Stable across 2013-2023 (88-100% per year). - Script 23 (intra-report consistency): for each 2-signer report, classify both signatures and check agreement. Firm A agrees 89.9% vs 62-67% at other Big-4. 87.5% Firm A reports have BOTH signers non-hand-signed; only 4 reports (0.01%) both hand-signed. ## New methodology additions - III-G: explicit within-auditor-year no-mixing identification assumption (supported by Firm A interview evidence). - III-H: 4th Firm A validation line: threshold-independent evidence from partner ranking + intra-report consistency. ## New results section IV-H (threshold-independent validation) - IV-H.1: Firm A year-by-year cosine<0.95 rate. 2013-2019 mean=8.26%, 2020-2023 mean=6.96%, 2023 lowest (3.75%). Stability contradicts partner's hypothesis that 2020+ electronic systems increase heterogeneity -- data shows opposite (electronic systems more consistent than physical stamping). - IV-H.2: partner ranking top-K tables (pooled + year-by-year). - IV-H.3: intra-report consistency per-firm table. ## Renumbering - Section H (was Classification Results) -> I - Section I (was Ablation) -> J - Tables XIII-XVI new (yearly stability, top-K pooled, top-10% per-year, intra-report), XVII = classification (was XII), XVIII = ablation (was XIII). These threshold-independent analyses address the codex review concern about circular validation by providing benchmark evidence that does not depend on any threshold calibrated to Firm A itself. 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.