gbanyan
4bb7aa9189
Codex independent peer review (paper/codex_review_gpt55_v3_18_1.md) audited
empirical claims against scripts/JSON reports rather than rubber-stamping
prior Accept verdicts. Verdict: Minor Revision. This commit addresses every
flagged item.
- Soften mechanism-identification language (Results IV-D.1, Discussion B):
per-signature cosine "fails to reject unimodality" rather than "reflects a
single dominant generative mechanism"; framing tied to joint evidence.
- Replace overabsolute "single stored image" with multi-template phrasing
in Introduction and Methodology III-A.
- Reframe Methodology III-H so practitioner knowledge is non-load-bearing;
evidentiary basis is the paper's own image evidence.
- Fix stale section cross-references after the v3.18 retitling: IV-F.* ->
IV-G.* in 11 locations across methodology and results.
- Fix 0.941 / 0.945 / 0.9407 wording in Methodology III-K to use the
calibration-fold P5 = 0.9407 and the rounded sensitivity cut 0.945.
- Soften "sharp discontinuity" in Results IV-G.3 to "23-28 percentage-point
gap consistent with firm-wide non-hand-signing practice".
- Soften Conclusion's "directly generalizable" with explicit conditions on
analogous anchors and artifact-generation physics.
- Add Appendix B: table-to-script provenance map (15 manuscript tables
mapped to generating scripts and JSON report artifacts).
- New script signature_analysis/28_byte_identity_decomposition.py produces
reproducible artifacts for two previously-unverified claims:
(a) 145 / 50 / 180 / 35 Firm A byte-identity decomposition (verified);
(b) cross-firm dual-descriptor convergence -- corrected from the previous
manuscript text "non-Firm-A 11.3% vs Firm A 58.7% (5x)" to the
database-verified "non-Firm-A 42.12% vs Firm A 88.32% (~2.1x)".
- Clarify scripts 19 / 21 docstrings: legacy EER / FRR / Precision / F1
helpers are retained for diagnostic use only and are NOT cited as
biometric performance in the paper. Remove "interview evidence" wording.
- Rebuild Paper_A_IEEE_Access_Draft_v3.docx.
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.