gbanyan
c79329457a
Splices v4 drafts into v3.20.0 master sub-files. Drops the "paper/v4/" working drafts and lands the v4.0 content in the master file structure. Internal draft notes / close-out checklists / open- questions blocks stripped at splice (per round-1 through round-6 deferral). Abstract (paper_a_abstract_v3.md): - Replaced v3.20.0 abstract (240w) with v4.0 abstract (247w). §I Introduction (paper_a_introduction_v3.md): - Replaced v3.20.0 §I with v4.0 §I (16 paragraphs + 8-item contributions list). §II Related Work (paper_a_related_work_v3.md): - Inserted v4.0 LOOO addition paragraph after the existing finite-mixture paragraph; added refs [42]-[44] to the internal reference annotation list. §III Methodology (paper_a_methodology_v3.md): - §III-A..F (Pipeline / Data / Page ID / Detection / Features / Dual Descriptors): kept v3.20.0 content unchanged. - §III-G..M: replaced v3.20.0 §III-G..K with v4.0 §III-G..M (Unit & Scope / Reference Populations / Distributional Diagnostics + composition decomposition / K=3 descriptive / Convergent internal-consistency / Anchor-based ICCR L.0-L.7 / Validation strategy + Table XXVII ten-tool collection). - §III-N Data Source & Anonymization: kept v3.20.0 §III-L content, renumbered to §III-N (after v4 §III-M). - §III-E ablation cross-reference: updated "§IV-I" -> "§IV-L" to match the renumbered §IV. - §III-F pixel-identity cross-reference: updated "§III-J" -> "§III-K". Gemini round-2 artifact paper/gemini_review_v4_round2.md also added (was uncommitted from the parallel-review batch). 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.