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Major changes (per partner red-pen + user decision): - Delete entire accountant-level analysis (III.J, IV.E, Tables VI/VII/VIII, Fig 4) -- cross-year pooling assumption unjustified, removes the implicit "habitually stamps = always stamps" reading. - Renumber sections III.J/K/L (was K/L/M) and IV.E/F/G/H/I (was F/G/H/I/J). - Title: "Three-Method Convergent Thresholding" -> "Replication-Dominated Calibration" (the three diagnostics do NOT converge at signature level). - Operational cosine cut anchored on whole-sample Firm A P7.5 (cos > 0.95). - Three statistical diagnostics (Hartigan/Beta/BD-McCrary) reframed as descriptive characterisation, not threshold estimators. - Firm A replication-dominated framing: 3 evidence strands -> 2. - Discussion limitation list: drop accountant-level cross-year pooling and BD/McCrary diagnostic; add auditor-year longitudinal tracking as future work. - Tone-shift: "we do not claim / do not derive" -> "we find / motivates". Reference verification (independent web-search audit of all 41 refs): - Fix [5] author hallucination: Hadjadj et al. -> Kao & Wen (real authors of Appl. Sci. 10:11:3716; report at paper/reference_verification_v3.md). - Polish [16] [21] [22] [25] (year/volume/page-range/model-name). Gemini 2.5 Pro peer review (Minor Revision verdict, A-F all positive): - Neutralize script-path references in tables/appendix -> "supplementary materials". - Move conflict-of-interest declaration from III-L to new Declarations section before References (paper_a_declarations_v3.md). 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.