gbanyan
0ff1845b22
Three blockers from codex gpt-5.4 round-3 review (codex_review_gpt54_v3_3.md):
B1 Classifier vs three-method threshold mismatch
- Methodology III-L rewritten to make explicit that the per-signature
classifier and the accountant-level three-method convergence operate
at different units (signature vs accountant) and are complementary
rather than substitutable.
- Add Results IV-G.3 + Table XII operational-threshold sensitivity:
cos>0.95 vs cos>0.945 shifts dual-rule capture by 1.19 pp on whole
Firm A; ~5% of signatures flip at the Uncertain/Moderate boundary.
B2 Held-out validation false "within Wilson CI" claim
- Script 24 recomputes both calibration-fold and held-out-fold rates
with Wilson 95% CIs and a two-proportion z-test on each rule.
- Table XI replaced with the proper fold-vs-fold comparison; prose
in Results IV-G.2 and Discussion V-C corrected: extreme rules agree
across folds (p>0.7); operational rules in the 85-95% band differ
by 1-5 pp due to within-Firm-A heterogeneity (random 30% sample
contained more high-replication C1 accountants), not generalization
failure.
B3 Interview evidence reframed as practitioner knowledge
- The Firm A "interviews" referenced throughout v3.3 are private,
informal professional conversations, not structured research
interviews. Reframed accordingly: all "interview*" references in
abstract / intro / methodology / results / discussion / conclusion
are replaced with "domain knowledge / industry-practice knowledge".
- This avoids overclaiming methodological formality and removes the
human-subjects research framing that triggered the ethics-statement
requirement.
- Section III-H four-pillar Firm A validation now stands on visual
inspection, signature-level statistics, accountant-level GMM, and
the three Section IV-H analyses, with practitioner knowledge as
background context only.
- New Section III-M ("Data Source and Firm Anonymization") covers
MOPS public-data provenance, Firm A/B/C/D pseudonymization, and
conflict-of-interest declaration.
Add signature_analysis/24_validation_recalibration.py for the recomputed
calib-vs-held-out z-tests and the classifier sensitivity analysis;
output in reports/validation_recalibration/.
Pending (not in this commit): abstract length (368 -> 250 words),
Impact Statement removal, BD/McCrary sensitivity reporting, full
reproducibility appendix, references cleanup.
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.