gbanyan
b33e20d479
Major Phase 4 prose update aligning narrative with the §III v7
anchor-based ICCR framework (codex rounds 29-34):
- Abstract (247 words, under 250 limit): replaced K=3 mixture +
natural-threshold framing with composition decomposition +
multi-level ICCR + firm heterogeneity. Positioning as
specificity-proxy-anchored screening framework.
- §I Introduction:
* Methodological-design paragraph rewritten (no natural threshold;
multi-level reporting; per-firm stratification; unsupervised
disclosure)
* Two new paragraphs documenting composition decomposition
overturning distributional path, and anchor-based three-unit
ICCR calibration
* Firm heterogeneity + within-firm collision concentration as
central findings
* Contribution list rewritten (8 items): composition decomposition
disproves natural threshold (NEW #4); multi-level ICCR
calibration (NEW #5); firm heterogeneity quantification (NEW #6);
K=3 demoted to descriptive partition (#7); multi-tool validation
ceiling positioning (#8)
- §V Discussion:
* §V-B retitled "composition-driven multimodality"; 2x2 factorial
decomposition reported
* §V-C Firm A reframed: position contrast + within-firm collision
pattern, not "templated-end calibration anchor"
* §V-D K=2/K=3 reframed as descriptive firm-compositional
partitions (no "mechanism boundary" language)
* §V-E three-score convergence reinterpreted as descriptor-position
ranking, not hand-leaning mechanism ranking
* §V-F (new title) Anchor-based multi-level calibration with all
three units of analysis
* §V-G expanded to 9 v4-specific limitations (no signature-level
ground truth; assumption-violation; scope; conservative-subset;
inherited rule components; deployed-rate excess not TPR; A1
stipulation; K=3 composition sensitivity; no partner-level
mechanism attribution) plus 5 inherited limitations
- §VI Conclusion: 8-point contribution list mirroring §I; 4 future
work directions including within-firm collision-mechanism
disambiguation and audit-quality companion analysis.
- Header draft-note updated to v3 (post codex rounds 26-34);
Phase 4 v2 changelog moved to CHANGELOG.md placeholder.
Companion to §III v7 commit 723a3f6.
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.