gbanyan
6946baa096
Codex gpt-5.4 round-5 (codex_review_gpt54_v3_5.md) verdict was Minor Revision - all v3.4 round-4 PARTIAL/UNFIXED items now confirmed RESOLVED, including line-by-line recomputation of Table XI z/p matching the manuscript values. This commit cleans the remaining quick-win items: Table IX numerical sync to Script 24 authoritative values - Five count corrections: cos>0.837 (60,405->60,408), cos>0.945 (57,131/94.52% -> 56,836/94.02%, was 295 sigs / 0.50 pp off), cos>0.973 (48,910/80.91% -> 48,028/79.45%, was 882 sigs / 1.46 pp off), cos>0.95 (55,916->55,922), dh<=8 (57,521->57,527), dh<=15 (60,345->60,348), dual (54,373->54,370). - Threshold label cos>0.941 -> cos>0.9407 (use exact calib-fold P5 rather than rounded value). - "dHash_indep <= 5 (calib-fold median-adjacent)" relabeled to "(whole-sample upper-tail of mode)" to match what III-L explains. - Added "(operational dual)" / "(style-consistency boundary)" labels for unambiguous mapping into III-L category definitions. - Removed circularity-language footnote inside the table comment. Circularity overclaim removed paper-wide - Methodology III-K (Section 3 anchor): "we break the resulting circularity" -> "we make the within-Firm-A sampling variance visible". - Results IV-G.2 subsection title: "(breaks calibration-validation circularity)" -> "(within-Firm-A sampling variance disclosure)". - Combined with the v3.5 Abstract / Conclusion edits, no surviving use of circular* anywhere in the paper. export_v3.py title page now single-anonymized - Removed "[Authors removed for double-blind review]" placeholder (IEEE Access uses single-anonymized review). - Replaced with explicit "[AUTHOR NAMES - fill in before submission]" + affiliation placeholder so the requirement is unmissable. - Subtitle now reads "single-anonymized review". III-G stale "cosine-conditional dHash" sentence removed - After the v3.5 III-L rewrite to dh_indep, the sentence at Methodology L131 referencing "cosine-conditional dHash used as a diagnostic elsewhere" no longer described any current paper usage. - Replaced with a positive statement that dh_indep is the dHash statistic used throughout the operational classifier and all reported capture-rate analyses. Abstract trimmed 247 -> 242 words for IEEE 250-word safety margin - "an end-to-end pipeline" -> "a pipeline"; "Unlike signature forgery" -> "Unlike forgery"; "we report" passive recast; small conjunction trims. Outstanding items deferred (require user decision / larger scope): - BD/McCrary either substantiate (Z/p table + bin-width robustness) or demote to supplementary diagnostic. - Visual-inspection protocol disclosure (sample size, rater count, blinding, adjudication rule). - Reproducibility appendix (VLM prompt, HSV thresholds, seeds, EM init / stopping / boundary handling). 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.