gbanyan
af08391a68
Gemini 3.1 Pro round-19 (paper/gemini_review_v3_18_4.md) caught FOUR
serious issues that all 18 prior AI review rounds missed, including
fabricated rationalizations and a real statistical flaw. All four
verified by direct DB / script inspection. Verdict: Major Revision; this
commit closes every flagged item.
Fabricated rationalization corrections (text only, numbers unchanged):
- Section IV-H "656 documents excluded" rewritten. Previous text claimed
the exclusion was because "single-signature documents have no same-CPA
pairwise comparison" -- a fabricated explanation that contradicts the
paper's cross-document matching methodology. The truth, verified
against signature_analysis/09_pdf_signature_verdict.py L44 (WHERE
s.is_valid = 1 AND s.assigned_accountant IS NOT NULL): the 656
documents are excluded because none of their detected signatures could
be matched to a registered CPA name (assigned_accountant IS NULL).
- Section IV-F.2 "two CPAs excluded for disambiguation ties" rewritten.
No disambiguation logic exists in script 24; the 178 vs 180 difference
comes from two registered Firm A partners being singletons in the
corpus (one signature each, so per-signature best-match cosine is
undefined and they do not appear in the matched-signature table that
feeds the 70/30 split).
- Appendix B Table XIII provenance corrected. The previous attribution
to 13_deloitte_distribution_analysis.py / accountant_similarity_analysis.json
was wrong: neither artifact has year_month grouping. New script
29_firm_a_yearly_distribution.py reproduces Table XIII exactly from
the database via accountants.firm + signatures.year_month grouping.
Statistical flaw corrections (numbers updated):
- Inter-CPA negative anchor rewritten in 21_expanded_validation.py. The
prior implementation drew 50,000 random cross-CPA pairs from a
LIMIT-3000 random subsample, reusing each signature ~33 times and
artificially tightening Wilson FAR confidence intervals on Table X.
The corrected implementation samples 50,000 i.i.d. pairs uniformly
across the full 168,755-signature matched corpus.
- Re-run script 21. Table X numbers are close to v3.18.4 but no longer
rest on the inflated-precision artifact:
cos > 0.837: FAR 0.2101 (was 0.2062), CI [0.2066, 0.2137]
cos > 0.900: FAR 0.0250 (was 0.0233), CI [0.0237, 0.0264]
cos > 0.945: FAR 0.0008 (unchanged at this resolution)
cos > 0.950: FAR 0.0005 (was 0.0007), CI [0.0003, 0.0007]
cos > 0.973: FAR 0.0002 (was 0.0003), CI [0.0001, 0.0004]
cos > 0.979: FAR 0.0001 (was 0.0002), CI [0.0001, 0.0003]
- Inter-CPA cosine summary stats also updated:
mean 0.763 (was 0.762)
P95 0.886 (was 0.884)
P99 0.915 (was 0.913)
max 0.992 (was 0.988)
- Manuscript IV-F.1 prose updated to reflect the i.i.d. full-corpus
sampling.
Rebuild Paper_A_IEEE_Access_Draft_v3.docx.
Note: this is v3.19.0 because v3.19 closes both fabrication and a
genuine statistical flaw, not just provenance polish.
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.