gbanyan
0471e36fd4
User review of the v3.15 Sanity Sample subsection revealed that the paper's claim of "inter-rater agreement with the classifier in all 30 cases" (Results IV-G.4) was not backed by any data artifact in the repository. Script 19 exports a 30-signature stratified sample to reports/pixel_validation/sanity_sample.csv, but that CSV contains only classifier output fields (stratum, sig_id, cosine, dhash_indep, pixel_identical, closest_match) and no human-annotation column, and no subsequent script computes any human--classifier agreement metric. User confirmed that the only human annotation in the project was the YOLO training-set bounding-box labeling; signature classification (stamped vs hand-signed) was done entirely by automated numerical methods. The 30/30 sanity-sample claim was therefore factually unsupported and has been removed. Investigation additionally revealed that the "independent visual inspection of randomly sampled Firm A reports reveals pixel-identical signature images...for many of the sampled partners" framing used as the first strand of Firm A's replication-dominated evidence (Section III-H first strand, Section V-C first strand, and the Conclusion fourth contribution) had the same provenance problem: no human visual inspection was performed. The underlying FACT (that Firm A contains many byte-identical same-CPA signature pairs) is correct and fully supported by automated byte-level pair analysis (Script 19), but the "visual inspection" phrasing misrepresents the provenance. Changes: 1. Results IV-G.4 "Sanity Sample" subsection deleted entirely (results_v3.md L271-273). 2. Methodology III-K penultimate paragraph describing the 30-signature manual visual sanity inspection deleted (methodology_v3.md L259). 3. Methodology Section III-H first strand (L152) rewritten from "independent visual inspection of randomly sampled Firm A reports reveals pixel-identical signature images...for many of the sampled partners" to "automated byte-level pair analysis (Section IV-G.1) identifies 145 Firm A signatures that are byte-identical to at least one other same-CPA signature from a different audit report, distributed across 50 distinct Firm A partners (of 180 registered); 35 of these byte-identical matches span different fiscal years." All four numbers verified directly from the signature_analysis.db database via pixel_identical_to_closest = 1 filter joined to accountants.firm. 4. Discussion V-C first strand (L41) rewritten analogously to refer to byte-level pair evidence with the same four verified numbers. 5. Conclusion fourth contribution (L21) rewritten to "byte-level pair analysis finding of 145 pixel-identical calibration-firm signatures across 50 distinct partners (Section IV-G.1)." 6. Abstract (L5): "visual inspection and accountant-level mixture evidence..." rewritten as "byte-level pixel-identity evidence (145 signatures across 50 partners) and accountant-level mixture evidence..." Abstract now at 250/250 words. 7. Introduction (L55): "visual-inspection evidence" relabeled "byte-level pixel-identity evidence" for internal consistency. 8. Methodology III-H penultimate (L164): "validation role is played by the visual inspection" relabeled "validation role is played by the byte-level pixel-identity evidence" for consistency. All substantive claims are preserved and now back-traceable to Script 19 output and the signature_analysis.db pixel_identical_to_closest flag. This correction brings the paper's descriptive language into strict alignment with its actual methodology, which is fully automated (except for YOLO training annotation, disclosed in Methodology Section III-B). 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.