gbanyan
4ee2efb5bb
Verdict: Minor Revision (corroborates Gemini round-1 and Opus round-1). Round-1 panel finding closure (codex round-8 audit): - Codex own round-7: 11 Major + 15 Minor → 21 CLOSED, 4 OPEN/PARTIAL (mostly splice items); M6 + new-issue-1 (refs [42]-[44]) SUPERSEDED (Gemini was right, codex round-7 was wrong about absence) - Gemini round-1: 5 Major + 3 Minor all CLOSED in main body - Opus round-1: M1-M4 CLOSED in manuscript body; some minors open Provenance verification (independent of Opus): - Within-firm any-pair from Table XXV: 98.8032 / 76.6529 / 83.7079 / 77.3723% — Opus arithmetic confirmed - Same-pair joint: 99.9558 / 97.7011 / 98.1818 / 96.9697% — confirms the 97.0-99.96% range - Pooled Big-4 any-pair ICCR 0.1102 verified from Script 43 report (16,578 / 150,453); Wilson 95% half-width 0.00158 reconciles - Per-pair conditional ICCR 0.234 verified from Script 40b (70 / 299) Round-2-induced / round-2-exposed concrete blockers (fixable): 1. Abstract now 261 words (M3 fix pushed over <=250 IEEE Access target); need 11+ word trim 2. §IV line 177 footnote miscategorizes §IV-M.5 as n=150,442 — §IV-M.5 / Tables XXIV-XXV actually use 150,453 vector-complete per Script 44 report; only §IV-D through §IV-J use 150,442 3. §IV-I line 161 stale cross-reference: "§IV-M Table XVI" should be "§IV-M Tables XXI-XXVI" — XVI is the K=3 firm cross-tab, pre-existing error exposed by the cascade Minor copy-edit residue (not blockers): §III line 131 + §IV Table XI line 104 "replicated vs not-replicated" binary-collapse label; internal-note staleness at §III lines 438/445, §IV line 3/370. No empirical reopening: codex confirms Opus M3 does not invalidate round-7's Major closures of M2 (Big-4 scope) or M11 (cross-scope reproducibility). Only round-7 minor reopened: m2 abstract margin. Phase 5 readiness: Partial — empirical core ready, no new statistical work required; copy-edit / factual-reference splice blockers remain. 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.