gbanyan
6d2eddb6e8
Codex round 24 returned Minor Revision: 3 Major CLOSED + 3 Major
PARTIAL + 4 Minor CLOSED + 2 Minor PARTIAL + 4 Editorial CLOSED
+ 1 Editorial OPEN. All 7 narrow residual fixes were §III-side
(I applied §IV fixes thoroughly in v3 but didn't mirror them to
§III v4).
§III v5 fixes:
1. Anonymisation leak repaired:
- "held-out-EY fold" -> "held-out-Firm-D fold" (L71)
- "Firms B (KPMG) and D (EY)" -> "Firms B and D" (L99)
2. K=3 LOOO weight drift 0.025 -> 0.023 at three sites
(L71, L115, L173 provenance table). Matches Script 37 max
C1 weight deviation and §IV v3 line 139.
3. §III-K positive-anchor paragraph cross-ref repaired:
"v3.x inter-CPA negative anchor (§III-J inherited; Table X)"
-> "(§IV-I, inheriting v3.20.0 §IV-F.1 Table X)".
4. §III-L five-way Likely-hand-signed band made inclusive:
"Cosine below the all-pairs KDE crossover threshold." ->
"Cosine at or below the all-pairs KDE crossover threshold
(cos <= 0.837)." Matches Script 42 and §IV:19.
5. Open question 1's pointer changed from current §IV-F (which
is Convergent Internal-Consistency Checks) to v3.20.0
Tables IX/XI/XII/XII-B + §IV-J descriptive proportions.
6. Provenance table: new row for full-dataset n=686 citing
Script 41 fulldataset_report.md.
7. Draft-note header refreshed: v3 -> v5; v4 -> v5 etc.;
"internal -- remove before submission" tag added.
§IV v3.1 fixes:
- Close-out checklist L262 stale "codex round 23" wording
updated to "rounds 21-24 and before partner Jimmy review".
- Close-out item 4 "in this v2" stale wording -> "in this v3".
- New item 5 added: internal author notes (this checklist +
§III cross-reference index + both files' draft-note headers)
are author working artefacts and should be moved/stripped
before partner / submission packaging.
Round 24 finding summary post-v5/v3.1:
Major: 3 CLOSED, 3 -> CLOSED (anonymisation + cross-ref +
table numbering note residuals)
Minor: 4 CLOSED, 2 -> CLOSED (weight drift 0.025 -> 0.023;
low-cosine inclusivity cos <= 0.837)
Editorial: 4 CLOSED, 1 PARTIAL (draft notes remain visible but
explicitly marked as internal-only "remove before
submission")
Phase 4 readiness: pending decision on whether to do one more
codex verification round (round 25) before drafting Abstract /
Intro / Discussion / Conclusion prose.
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.