gbanyan
e36c49d2d8
Phase 4 first-pass draft replacing the v3.20.0 Abstract,
§I Introduction, §II Related Work, §V Discussion, and §VI
Conclusion blocks with the Big-4 reframed v4.0 prose. Single
consolidated file at paper/v4/paper_a_prose_v4_phase4.md.
Structure:
Abstract (~235 words, IEEE Access target <= 250)
§I Introduction (8-item contributions list updated for v4)
§II Related Work (mostly inherited; LOOO citation added)
§V Discussion (7 sub-sections: A-G covering distinct-problem
framing, accountant-level multimodality,
Firm A as templated-end case study, K=2
firm-mass conflation, K=3 reproducible shape,
three-score internal-consistency, pixel-
identity + inter-CPA validation, limitations)
§VI Conclusion + Future Work (4 future directions)
Key reframing decisions baked into the prose:
- Abstract leads with Big-4 scope + dip-test multimodality +
K=3 reproducibility + three-score convergence + 0% miss
rate + full-dataset robustness.
- §I positions the Big-4 sub-corpus scope as the
methodologically privileged calibration unit ("smallest
tested scope at which a finite-mixture model is
statistically supportable").
- §I-Contribution-4: Big-4 scope as substantive methodological
finding (was v3.x "percentile-anchored operational
threshold").
- §I-Contribution-5: K=3 mixture as descriptive (was v3.x
"distributional characterisation" framing).
- §I-Contribution-6: three-score convergent internal-
consistency (NEW in v4).
- §I-Contribution-8: full-dataset robustness as light
secondary scope (NEW in v4).
- §V-D: explicit "K=2 is firm-mass driven; K=3 is
reproducible in shape" framing — preempts the LOOO
reviewer attack vector codex round 23 first flagged.
- §V-G Limitations: seven explicit limitations including no
signature-level hand-signed ground truth, pixel-identity
conservative subset, MC band not separately v4-validated.
- §VI Future Work: four directions including a Paper B
placeholder for audit-quality companion analysis.
The technical §III v6 + §IV v3.2 are the foundation; this Phase
4 draft aligns the narrative with the codex-converged
methodology and results.
6 close-out items flagged at end of file (word-count check,
contribution count, LOOO citation, limitations grouping, Paper B
cross-ref, draft note stripping).
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.