gbanyan
dbe2f676bf
paper/gemini_partner_redpen_audit_v3_19_0.md: focused audit evaluating whether the partner's hand-marked red-pen review of v3.17 (4 themes, 11 specific items) has been adequately addressed in the current v3.19.0 draft. Cleaned from raw output (CLI 429 retry noise stripped). Result: 8/11 RESOLVED, 3/11 N/A (the underlying text/analysis was entirely removed in v3.18+: accountant-level BD/McCrary, the 139/32 C1/C2 split, and ZH/EN dual-language scaffolding). 0 remain UNRESOLVED, PARTIAL, or merely IMPROVED. Themes: - Theme 1 (citation reality): RESOLVED via reference_verification_v3.md and the [5] Hadjadj -> Kao & Wen correction in v3.18. - Theme 2 (AI-sounding prose): RESOLVED at every flagged spot — A1 stipulation rewritten as cross-year pair-existence with three concrete not-guaranteed conditions; conservative structural-similarity reduced to one literal sentence; IV-G validation lead-in now explicitly motivates each subsection. - Theme 3 (ZH/EN alignment): N/A — v3.19.0 is monolingual English for IEEE submission; the dual-language scaffolding that produced the gap no longer exists. - Theme 4 (specific numbers): all addressed — 92.6% match rate is now purely descriptive; 0.95 cut-off explicitly anchored on Firm A P7.5; Hartigan dip test correctly described as "more than one peak"; BIC forced-fit framing made blunt; 139/32 + accountant-level BD/McCrary removed. Gemini's bottom line: "smallest residual set of polish required before the partner re-read is empty." Manuscript is ready to send back to partner. 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.