gbanyan
9d19ca5a31
Major fixes per codex (gpt-5.4) review: ## Structural fixes - Fixed three-method convergence overclaim: added Script 20 to run KDE antimode, BD/McCrary, and Beta mixture EM on accountant-level means. Accountant-level 1D convergence: KDE antimode=0.973, Beta-2=0.979, LogGMM-2=0.976 (within ~0.006). BD/McCrary finds no transition at accountant level (consistent with smooth clustering, not sharp discontinuity). - Disambiguated Method 1: KDE crossover (between two labeled distributions, used at signature all-pairs level) vs KDE antimode (single-distribution local minimum, used at accountant level). - Addressed Firm A circular validation: Script 21 adds CPA-level 70/30 held-out fold. Calibration thresholds derived from 70% only; heldout rates reported with Wilson 95% CIs (e.g. cos>0.95 heldout=93.61% [93.21%-93.98%]). - Fixed 139+32 vs 180: the split is 139/32 of 171 Firm A CPAs with >=10 signatures (9 CPAs excluded for insufficient sample). Reconciled across intro, results, discussion, conclusion. - Added document-level classification aggregation rule (worst-case signature label determines document label). ## Pixel-identity validation strengthened - Script 21: built ~50,000-pair inter-CPA random negative anchor (replaces the original n=35 same-CPA low-similarity negative which had untenable Wilson CIs). - Added Wilson 95% CI for every FAR in Table X. - Proper EER interpolation (FAR=FRR point) in Table X. - Softened "conservative recall" claim to "non-generalizable subset" language per codex feedback (byte-identical positives are a subset, not a representative positive class). - Added inter-CPA stats: mean=0.762, P95=0.884, P99=0.913. ## Terminology & sentence-level fixes - "statistically independent methods" -> "methodologically distinct methods" throughout (three diagnostics on the same sample are not independent). - "formal bimodality check" -> "unimodality test" (dip test tests H0 of unimodality; rejection is consistent with but not a direct test of bimodality). - "Firm A near-universally non-hand-signed" -> already corrected to "replication-dominated" in prior commit; this commit strengthens that framing with explicit held-out validation. - "discrete-behavior regimes" -> "clustered accountant-level heterogeneity" (BD/McCrary non-transition at accountant level rules out sharp discrete boundaries; the defensible claim is clustered-but-smooth). - Softened White 1982 quasi-MLE claim (no longer framed as a guarantee). - Fixed VLM 1.2% FP overclaim (now acknowledges the 1.2% could be VLM FP or YOLO FN). - Unified "310 byte-identical signatures" language across Abstract, Results, Discussion (previously alternated between pairs/signatures). - Defined min_dhash_independent explicitly in Section III-G. - Fixed table numbering (Table XI heldout added, classification moved to XII, ablation to XIII). - Explained 84,386 vs 85,042 gap (656 docs have only one signature, no pairwise stat). - Made Table IX explicitly a "consistency check" not "validation"; paired it with Table XI held-out rates as the genuine external check. - Defined 0.941 threshold (calibration-fold Firm A cosine P5). - Computed 0.945 Firm A rate exactly (94.52%) instead of interpolated. - Fixed Ref [24] Qwen2.5-VL to full IEEE format (arXiv:2502.13923). ## New artifacts - Script 20: accountant-level three-method threshold analysis - Script 21: expanded validation (inter-CPA anchor, held-out Firm A 70/30) - paper/codex_review_gpt54_v3.md: preserved review feedback Output: Paper_A_IEEE_Access_Draft_v3.docx (391 KB, rebuilt from v3.1 markdown sources). 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.