gbanyan
d4f370bd5e
Spike checkpoint in response to codex rounds 28-30 review:
- 39b/c: signature-level dip test on Big-4 and non-Big-4 marginals
- 39d: dHash discrete-value robustness (raw vs jittered + histogram
valleys + firm residualization); confirms within-firm dHash dip
rejection is integer-mass-point artefact
- 39e: dHash firm-residualized + jittered 2x2 factorial decomposition;
confirms Big-4 pooled dh "multimodality" is composition + integer
artefact (centered + jittered p=0.35, 0/5 seeds reject)
- 40b: inter-CPA per-pair FAR sweep (cos + dh marginal + joint +
conditional); replicates v3 cos>0.95 FAR=0.0006 and provides
v4-new dh FAR curve
- 43: pool-normalized per-signature FAR (codex round-30 fix for
per-pair vs per-signature conflation); per-sig FAR for deployed
any-pair rule = 11.02%, per-firm structure shows Firm A 20% vs
B/C/D <1%
These scripts replace the distributional path (K=3 mixture / dip /
antimode) with anchor-based threshold derivation. Companion
artefacts in reports/v4_big4/{signature_level_diptest,
midsmall_signature_diptest, dhash_discrete_robustness,
inter_cpa_far_sweep, pool_normalized_far}/.
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.