gbanyan
4efbb7f4b8
Codex round-4 verification (commit bbb662a) disposition: Minor revision.
All prior blockers and majors confirmed resolved. Three small items remained.
MINOR (1 of 2 addressed in markdown source):
- Appendix rendered AFTER References (combined L1132 vs L1227), but IEEE
convention places appendices BEFORE references. Swapped concatenation
order in combined-file regeneration: abstract -> intro -> related_work
-> methodology -> results -> discussion -> conclusion -> APPENDIX ->
REFERENCES -> declarations -> impact. Combined file now has Appendix A
at L1132 and References at L1194.
MINOR (deferred to typesetting):
- Table A.II is prose-heavy for IEEE double-column layout. This is a
table-formatting concern for the LaTeX/DOCX export step (table*, smaller
font, or column-break adjustments), not a markdown-source issue.
Documenting as a known typesetting consideration for the export pipeline.
NIT:
- Table A.II referenced "§IV-M.4 footnote" but the content at §IV-M.4
L1007 is inline prose, not a footnote. Changed to "(§IV-M.4)".
Artefacts:
- Combined manuscript regenerated: paper_a_v4_combined.md, 1316 lines.
- Appendix A.1 (BD/McCrary) + A.2 (Diagnostic Summary) precede References.
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.