gbanyan
623eb4cd4b
Codex GPT-5.5 cross-verified the Gemini partner red-pen audit (paper/codex_partner_redpen_audit_v3_19_0.md) and downgraded item (j) -- the BIC strict-3-component upper-bound framing -- from RESOLVED to IMPROVED, because the "upper bound" wording the partner originally red-circled in v3.17 still survived in two methodology sentences and one Table VI row label, even though Section IV-D.3 had been retitled "A Forced Fit" in v3.18. This commit closes that residual: - Methodology III-I.2: "the 2-component crossing should be treated as an upper bound rather than a definitive cut" -> "we report the resulting crossing only as a forced-fit descriptive reference and do not use it as an operational threshold". - Methodology III-I.4: "should be read as an upper bound rather than a definitive cut" -> "reported only as a descriptive reference rather than as an operational threshold". - Table VI row "0.973 (signature-level Beta/KDE upper bound)" relabelled to "0.973 (signature-level Beta/KDE forced-fit reference)" to match the IV-D.3 "Forced Fit" framing. - reference_verification_v3.md header updated so the [5] entry reads as an audit trail of a fix already applied (v3.18 reference list reflects every correction) rather than as an active major problem. - Rebuild Paper_A_IEEE_Access_Draft_v3.docx. Also commits the codex partner-redpen audit artifact so the disagreement trail with Gemini is preserved. 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.