gbanyan
4c3bcfa288
paper/gemini_review_v3_19_0.md: 45 lines (cleaned from raw output that included CLI 429 retry noise). Gemini round-20 confirmed all four round-19 Major Revision findings are RESOLVED in v3.19.0: - 656-document exclusion explanation: VERIFIED-AGAINST-ARTIFACT (matches 09_pdf_signature_verdict.py L44 filtering logic). - Table XIII provenance: VERIFIED-AGAINST-ARTIFACT (deterministically reproduced by new 29_firm_a_yearly_distribution.py). - 2-CPA disambiguation rewrite: VERIFIED-AGAINST-ARTIFACT (matches the NULL filter in 24_validation_recalibration.py). - Inter-CPA negative anchor: VERIFIED-AGAINST-ARTIFACT (50k i.i.d. pairs from full 168k matched corpus, no LIMIT-3000 sub-sample). Verdict: Accept. "None required. The manuscript is methodologically sound, narratively disciplined, and ready for publication as-is." This is the first Accept verdict in the 20-round cycle that comes directly after a Major Revision (round 19) was fully processed. Prior Accepts (round 7 Gemini, round 15 Gemini) were both later overturned by codex on independent re-audit. The current state has the strongest evidence base in the cycle: 4 distinct artifact verifications behind each previously fabricated claim. Remaining UNVERIFIABLE-but-acceptable items (758 CPAs / 15 doc types, Qwen2.5-VL config, YOLO metrics, 43.1 docs/sec throughput) are now classified by Gemini as "non-critical context" — supplement-material candidates but not main-paper review blockers. 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.