gbanyan
becce857e1
Codex GPT-5.5 3-axis peer review (paper/codex_review_gpt55_v4_round_3axis.md) identified 11 MAJOR + 5 MINOR + 0 BLOCKER on three axes: (1) abstract/body tone consistency, (2) methodology clarity / v3 residue, (3) no implicit within-CPA or cross-year signature-consistency assumptions. 13 patches applied across 4 source files; mirrored in paper_a_v4_combined.md. Axis 1 (tone consistency between abstract and body): - S I L33: "resolves the ambiguity" -> "provides complementary evidence for screening cases where ... hypotheses diverge" - S I L35: "disproves the distributional-threshold path" -> "does not support the distributional-threshold path" - S I L37 / S V-F L29: "characterise the deployed five-way classifier at three units" -> "characterise the deployed HC sub-rule and document-level HC+MC alarm derived from the five-way classifier at three units" (consistent with S V-H which says only HC sub-rule and HC+MC alarm are re-characterised by the present ICCR battery) - S I L39 / S V-C / S III-L.4: "consistent with firm-specific template, stamp, or document-production reuse mechanisms" -> "consistent with -- but does not independently establish -- firm-level template-like reuse, digitisation-pipeline homogeneity, or signing-style homogeneity, which descriptor-only data cannot separate (S V-H)" (mirrors abstract) Axis 2 (methodology clarity / v3 residue): - S III-G: added unit-bridge sentence distinguishing "descriptor-summary units" (signature/accountant) from "operational reporting units" (per-comparison/per-signature/per-document, S III-L) - S III-H.2: "The calibration distinguishes two reference populations" -> "The supporting diagnostics use two reference populations" with explicit "neither is the calibration anchor" - S III-L.1: "specificity" -> "ICCR refinement" - S III-L.2: added "descriptive intuition, not an independence assumption used for estimation" caveat after the 1-(1-p)^n form Axis 3 (no implicit signature-consistency assumptions): - S III-F: hand-signing motivation rewritten as working hypothesis that "the classifier does not require ... to hold for all CPAs" - S III-G A1: added "A1 does not assume temporal stability of handwriting or scanning workflow within or across years" - S III-H.1: added label-caveat paragraph (operational rule outputs, not validated ground-truth classes); HC "strong replication evidence" -> "image-similarity evidence consistent with replication"; HSC "consistent with a CPA who signs very consistently" -> "mechanism not resolved by descriptor data alone"; LH explicitly owns that cross-year handwriting drift, scanner workflow change, or template variant rotation can also yield low max-cosine within a same-CPA pool - S III-L.6 / S IV-M.6: "same-CPA repeatability signal" -> "observed same-CPA-pool excess ... not attributed to within-CPA handwriting repeatability" Deferred (structural, not single-sentence patch): codex S III-I.2 / S III-J K=2/K=3 deduplication; codex S III-K LOOO / S III-J duplication. Both are MINOR stylistic redundancies, not reviewer-rejection risks. DOCX rebuilt via export_v3.py; v4.0_20260515 file refreshed. 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.