gbanyan
615059a2c1
Opus round-9 review (paper/opus_final_review_v3_9.md) dissented from Gemini round-7 Accept and aligned with codex round-8 Minor, but for a DIFFERENT issue all prior reviewers missed: the paper's main text in four locations flatly claimed the BD/McCrary accountant-level null "persists across the Appendix-A bin-width sweep", yet Appendix A Table A.I itself documents a significant accountant-level cosine transition at bin 0.005 with |Z_below|=3.23, |Z_above|=5.18 (both past 1.96) located at cosine 0.980 --- on the upper edge of our two threshold estimators' convergence band [0.973, 0.979]. This is a paper-to-appendix contradiction that a careful reviewer would catch in 30 seconds. BLOCKER B1: BD/McCrary accountant-level claim softened across all four locations to match what Appendix A Table A.I actually reports: - Results IV-D.1 (lines 85-86): rewritten to say the null is not rejected at 2/3 cosine bin widths and 2/3 dHash bin widths, with the one cosine transition at bin 0.005 sitting on the upper edge of the convergence band and the one dHash transition at |Z|=1.96. - Results IV-E Table VIII row (line 145): "no transition / no transition" changed to "0.980 at bin 0.005 only; null at 0.002, 0.010" / "3.0 at bin 1.0 only ( |Z|=1.96); null at 0.2, 0.5". - Results IV-E line 130 (Third finding): "does not produce a significant transition (robust across bin-width sweep)" replaced with "largely null at the accountant level --- no significant transition at 2/3 cosine bin widths and 2/3 dHash bin widths, with the one cosine transition at bin 0.005 sitting at cosine 0.980 on the upper edge of the convergence band". - Results IV-E line 152 (Table VIII synthesis paragraph): matched reframing. - Discussion V-B (line 27): "does not produce a significant transition at the accountant level either" -> "largely null at the accountant level ... with the one cosine transition on the upper edge of the convergence band". - Conclusion (line 16): matched reframing with power caveat retained. MAJOR M1: Related Work L67 stale "well suited to detecting the boundary between two generative mechanisms" framing (residue from pre-demotion drafts) replaced with a local-density-discontinuity diagnostic framing that matches the rest of the paper and flags the signature-level bin-width sensitivity + accountant-level rarity as documented in Appendix A. MAJOR M2: Table XII orphaned in-text anchor --- Table XII is defined inside IV-G.3 but had no in-text "Table XII reports ..." pointer at its presentation location. Added a single sentence before the table comment. MINOR m1: Section IV-I.1 "4 of 30,000+ Firm A documents, 0.01%" replaced with the exact "4 of 30,226 Firm A documents, 0.013%". MINOR m2: Section IV-E "the two-dimensional two-component GMM" wording ambiguity (reader might confuse with the already-selected K*=3 GMM from BIC) replaced with explicit "a separately fit two-component 2D GMM (reported as a cross-check on the 1D accountant-level crossings)". MINOR m3: Section IV-D L59 "downstream all-pairs analyses (Tables XII, XVIII)" misnomer --- Table XII is per-signature classifier output not all-pairs; Table XVIII's all-pairs are over ~16M pairs not 168,740. Replaced with an accurate list: "same-CPA per-signature best-match analyses (Tables V and XII, and the Firm-A per-signature rows of Tables XIII and XVIII)". MINOR m4: Methodology III-H L156 "the validation role is played by ... the held-out Firm A fold" slightly overclaims what the held-out fold establishes (the fold-level rates differ by 1-5 pp with p<0.001). Parenthetical hedge added: "(which confirms the qualitative replication-dominated framing; fold-level rate differences are disclosed in Section IV-G.2)". Also add: - paper/opus_final_review_v3_9.md (Opus 4.7 max-effort review) - paper/gemini_review_v3_8.md (Gemini round-7 Accept verdict, was missing from prior commit) Abstract remains 243 words (under IEEE Access 250 limit). 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.