gbanyan
cb77f481ec
Three targeted fixes per partner's red-pen audit (residue from v3.18 cleanup):
1. III-D 92.6% match rate -- partner red-circled the bare figure ("不太懂改善線").
Add explicit explanation of the unmatched 7.4% (13,573 signatures): they
could not be matched to a registered CPA name (deviation from two-signature
layout, OCR-name mismatch) and are excluded from same-CPA pairwise analyses
for definitional reasons, not discarded as noise.
2. III-I.1 Hartigan dip-test wording -- partner wrote "?所以為何?" next to the
"rejecting unimodality is consistent with but does not directly establish
bimodality" sentence. Replace with a direct three-line explanation: the
test asks "is the distribution single-peaked?", a non-significant p means
we cannot reject single-peak, a significant p means more than one peak
(could be 2/3/...). Removes the partner's confusion without losing rigor.
3. IV-G validation lead-in -- partner wrote "不太懂為何陳述?" on the
tangled "consistency check / threshold-free / operational classifier"
triple. Rewrite as a three-bullet structure that names the *informative
quantity* in each subsection (temporal trend / concentration ratio /
cross-firm gap) and states explicitly why each is robust to cutoff choice.
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.