gbanyan
f1c253768a
Codex round-17 (paper/codex_review_gpt55_v3_18_2.md) re-audited v3.18.2 and flagged three new issues introduced by the v3.18.2 edits themselves plus items it had partially RESOLVED but not fully cleaned up. Verdict still Minor Revision; this commit closes the new findings. - Fix Appendix B provenance paths: replace four fabricated paths (formal_statistical/*, deloitte_distribution/*, pdf_level/*, ablation/*) with the actual artifact paths verified in the local report tree. - Acknowledge that the report tree is at /Volumes/NV2/PDF-Processing/... and reviewers should rebase to their own report root rather than rely on absolute paths. - Remove residual "single dominant mechanism" wording from Methodology III-H (third primary evidence sentence) and Discussion V-C. - Fix Methodology III-H Hartigan dip-test parenthetical: "p = 0.17 at n >= 10 signatures" wrongly attached the accountant-level filter to the signature-level dip; corrected to "p = 0.17, N = 60,448 Firm A signatures". - Soften Introduction Firm A motivation: replace "widely recognized within the audit profession as making substantial use of non-hand-signing for the majority of its certifying partners" with a methodology-first framing that defers to the image evidence reported in the paper. - Soften Methodology III-H "widely held within the audit profession" wording (kept as motivation, marked clearly as non-load-bearing in the next sentence). - Reconcile 55,921 vs 55,922 Firm A cosine-only counts in Section IV-H.2: document explicitly that the one-record drift comes from successive DB snapshots used to materialize Table IX vs the new script-28 artifact; no rate at two decimal places is affected. - 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.