gbanyan
6ab6e19137
User flagged that the Experimental Setup claim "All experiments were conducted on a workstation equipped with an Apple Silicon processor with Metal Performance Shaders (MPS) GPU acceleration" was factually inaccurate: YOLOv11 training/inference and ResNet-50 feature extraction were actually performed on an Nvidia RTX 4090 (CUDA), and only the downstream statistical analyses ran on Apple Silicon/MPS. Rewrote Section IV-A (Experimental Setup) to describe the mixed hardware honestly: - Nvidia RTX 4090 (CUDA): YOLOv11n signature detection (training + inference on 90,282 PDFs yielding 182,328 signatures); ResNet-50 forward inference for feature extraction on all 182,328 signatures - Apple Silicon workstation with MPS: downstream statistical analyses (KDE antimode, Hartigan dip test, Beta-mixture EM with logit- Gaussian robustness check, 2D GMM, BD/McCrary diagnostic, pairwise cosine/dHash computations) Added a closing sentence clarifying platform-independence: because all steps rely on deterministic forward inference over fixed pre- trained weights (no fine-tuning) plus fixed-seed numerical procedures, reported results are platform-independent to within floating-point precision. This pre-empts any reader concern about the mixed-platform execution affecting reproducibility. This correction is consistent with the v3.16 integrity standard (all descriptions must back-trace to reality): where v3.16 fixed the fabricated "human-rater sanity sample" and "visual inspection" claims, v3.17 fixes the similarly inaccurate hardware description. No substantive results change. 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.