gbanyan
9b0b8358a2
Round-11 Gemini 3.1 Pro fresh full-paper review (Minor Revision) surfaced four issues that the prior 10 rounds (codex gpt-5.4 x4, codex gpt-5.5 x1, Gemini 3.1 Pro x2, Opus 4.7 x1, paragraph-level v3.11 review) all missed: 1. MAJOR - Percentile-terminology contradiction between Section III-L L290 and Section III-H L160. III-L called 0.95 the "whole-sample Firm A P95" of the per-signature best-match cosine distribution, but III-H states 92.5% of Firm A signatures exceed 0.95. Under standard bottom-up percentile convention this makes 0.95 the P7.5, not the P95; Table XI calibration-fold data (Firm A cosine median = 0.9862, P5 = 0.9407) confirms true P95 is near 0.998. Fix: rewrote III-L L290 to state 0.95 corresponds to approximately the whole-sample Firm A P7.5 with the 92.5%/7.5% complement stated explicitly. dHash P95 claims elsewhere (Table XI, L229/L233) were already correct under standard convention and are unchanged. 2. MINOR - Firm A CPA count inconsistency. Discussion V-C L44 said "Nine additional Firm A CPAs are excluded from the GMM for having fewer than 10 signatures" but Results IV-G.2 L216 defines 178 valid Firm A CPAs (180 registry minus 2 disambiguation-excluded); 178 - 171 = 7. Fix: corrected to "seven are outside the GMM" with explicit 178-baseline and cross-reference to IV-G.2. 3. MINOR - Table XVI mixed-firm handling broken promise. Results L355-356 previously said "mixed-firm reports are reported separately" but Table XVI only lists single-firm rows summing to exactly 83,970, and no subsequent prose reports the 384 mixed-firm agreement rate. Fix: rewrote L355-356 to state Table XVI covers the 83,970 single-firm reports only and that the 384 mixed-firm reports (0.46%) are excluded because firm-level agreement is not well defined when the two signers are at different firms. 4. MINOR - Contribution-count structural inconsistency. Introduction enumerates seven contributions, Conclusion opens with "Our contributions are fourfold." Fix: rewrote the Conclusion lead to "The seven numbered contributions listed in Section I can be grouped into four broader methodological themes," making the grouping explicit. No re-computation. All tables (IV-XVIII) and Appendix A numbers unchanged. Abstract unchanged (still 248/250 words). 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.