gbanyan
53125d11d9
Substantive content (addresses partner Jimmy's 2026-04-27 review of v3.19.1): Must-fix items (6/6): - §III-F SSIM/pixel rejection rewritten from first principles (design-level argument from luminance/contrast/structure local-window product, not the prior empirical 0.70 result) - Table VI restructured by population × method; added missing Firm A logit-Gaussian-2 0.999 row; KDE marked undefined (unimodal), BD/McCrary marked bin-unstable (Appendix A) - Tables IX / XI / §IV-F.3 dHash 5/8/15 inconsistency resolved: ≤8 demoted from "operational dual" to "calibration-fold-adjacent reference"; the actual classifier rule cos>0.95 AND dH≤15 = 92.46% added throughout - New Fig. 4 (yearly per-firm best-match cosine, 5 lines, 2013-2023, Firm A on top); script 30_yearly_big4_comparison.py - Tables XIV / XV extended with top-20% (94.8%) and top-30% (81.3%) brackets - §III-K reframed P7.5 from "round-number lower-tail boundary" to operating point; new Table XII-B (cosine-FAR-capture tradeoff at 5 thresholds: 0.9407 / 0.945 / 0.95 / 0.977 / 0.985) Nice-to-have items (3/3): - Table XII expanded to 6-cut classifier sensitivity grid (0.940-0.985) - Defensive parentheticals (84,386 vs 85,042; 30,226 vs 30,222) moved to table notes; cut "invite reviewer skepticism" and "non-load-bearing" Codex 3-pass verification cleanup: - Stale 0.973/0.977/0.979 references unified on canonical 0.977 (Firm A Beta-2 forced-fit crossing from beta_mixture_results.json) - dHash≤8 wording corrected to P95-adjacent (P95 = 9, ≤8 is the integer immediately below) instead of misleading "rounded down" - Table XII-B prose corrected: per-segment qualification of "non-Firm-A capture falls faster" (true on 0.95→0.977 segment but contracts on 0.977→0.985 segment); arithmetic now from exact counts Within-year analyses removed: - Within-year ranking robustness check (Class A) was added in nice-to-have pass but contradicts v3.14 A2-removal stance; removed from §IV-G.2 + the Appendix B provenance row - Within-CPA future-work disclosures (Class B) removed from Discussion limitation #5 and Conclusion future-work paragraph; subsequent limitations renumbered Sixth → Fifth, Seventh → Sixth DOCX rendering pipeline overhaul (paper/export_v3.py): Critical fix - every v3 DOCX since v3.0 was shipping WITHOUT TABLES: strip_comments() was wholesale-deleting HTML comments, but every numerical table is wrapped in <!-- TABLE X: ... -->, so the table body was deleted alongside the wrapper. Now unwraps TABLE comments (emit synthetic __TABLE_CAPTION__: marker + table body) while still stripping non-TABLE editorial comments. Result: 19 tables now render in the DOCX. Other rendering fixes: - LaTeX → Unicode conversion (50+ token replacements: Greek alphabet, ≤≥, ×·≈, →↔⇒, etc.); \frac/\sqrt linearisation; TeX brace tricks ({=}, {,}) - Math-context-scoped sub/superscript via PUA sentinels (/): no more underscore-eating in identifiers like signature_analysis - Display equations rendered via matplotlib mathtext to PNG (3 equations: cosine sim, mixture crossing, BD/McCrary Z statistic), embedded as numbered equation blocks (1), (2), (3); content-addressed cache at paper/equations/ (gitignored, regenerable) - Manual numbered/bulleted list rendering with hanging indent (replaces python-docx style="List Number" which silently drops the number prefix when no numbering definition is bound) - Markdown blockquote (> ...) defensively stripped - Pandoc footnote ([^name]) markers no longer leak (inlined at source) - Heading text cleaned of LaTeX residue + PUA sentinels - File paths in body text (signature_analysis/X.py, reports/Y.json) trimmed to "(reproduction artifact in Appendix B)" pointers New leak linter: paper/lint_paper_v3.py - two-pass markdown source + rendered DOCX leak detector; auto-runs at end of export_v3.py. Script changes: - 21_expanded_validation.py: added 0.9407, 0.977, 0.985 to canonical FAR threshold list so Table XII-B is reproducible from persisted JSON - 30_yearly_big4_comparison.py: NEW; generates Fig. 4 + per-firm yearly data (writes to reports/figures/ and reports/firm_yearly_comparison/) - 31_within_year_ranking_robustness.py: NEW; supports the within-year robustness check (no longer cited in paper but kept as repo-internal due-diligence artifact) Partner handoff DOCX shipped to ~/Downloads/Paper_A_IEEE_Access_Draft_v3.20.0_20260505.docx (536 KB: 19 tables + 4 figures + 3 equation images). 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.