gbanyan
1eb323e959
- Restrict the calibration negative anchor to Firms B/C/D, fiscal years 2013-2019 (pre-electronic-signature hand-signing period); B/C/D adopted e-signing post-2020 at staggered times, so 2013-2019 is the construct-clean baseline. Firm A scored across its full 2013-2023 record against it. - New locked numbers (codex-audited, Scripts 54/55): per-comparison HC floor 0.000010; per-signature HC floor 0.0059 [boot 0.0045-0.0073]; per-document HC 0.0117 / HC+MC 0.1753; per-firm HC+MC B 0.162 / C 0.225 / D 0.089. Firm A observed 0.817 = ~139x the clean floor (was ~70x on all-period BCD); Firm A out-of-sample vs clean pool 0.0001 (below floor -> never resembles genuine hand-signing). BCD 2020+ robustness: per-sig 0.0105, per-comparison 0.000036 (~2x pre-2020) quantifies the e-signing contamination. - Propagated through abstract / Sec. I / III-L / IV-M / V / conclusion; 0.837 crossover kept corpus-wide; ABCD retained as contamination comparison. - Grounded the 2013-2019 choice on data (floor drift) + e-sign-adoption background, not on in-text interview claims (double-blind). - Add Scripts 54 (temporal floor stability) and 55 (BCD 2013-2019 primary calibration + Firm A scoring). 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.