gbanyan
1e8466f7a8
Finishes the BCD re-anchor chassis (audit critique #1): Firm A was inconsistently framed as both a "within-Big-4 case study" and an "out-of-sample target". Harmonised to a single label, "out-of-sample templated-end target" (held out of the calibration negative anchor; scored against the normative Firms-B/C/D baseline), across: - §I contribution #3 (title + body) - §III-H.2 (opening trio BCD/Firm-A/non-Big-4; sub-header; role sentence) - §V-C body (removed the dual case-study/out-of-sample phrasing) (§V-C header already fixed in ac3372d.) Zero "case study" wording remains; no numbers changed. codex gpt-5.5 focused check: all consistency items PASS, no new findings. Also restore the BCD+non-Big-4 joint ICCR Wilson CI [0.000001, 0.000015] to the §IV-M Table XXI note (three-scope CI symmetry; the one MINOR completeness gap surfaced by a codex old-vs-new content diff, which otherwise confirmed no substantive content was dropped by the trim). Co-Authored-By: Claude Opus 4.8 (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.