gbanyan
10c82fd446
Codex round 26 returned Major Revision on Phase 4 v1: 9 Major
findings + 12 Minor + reviewer-attack vulnerabilities. v2
applies all flagged corrections.
Abstract changes:
- "Three independent feature-derived scores" -> "Three
feature-derived scores ... not statistically independent
because all three are functions of the same descriptor
pair". Names the operational output as the inherited
five-way classifier.
- Trimmed from 277 to ~245 words to stay within IEEE Access
250-word limit while keeping all numerical anchors.
§I Introduction:
- Line 29 cross-ref §III-D -> §III-G through §III-J
(§III-D was wrong; the methodology lives in §III-G/I/J).
- Big-4 scope claim narrowed: "neither any single firm pooled
alone nor the broader full-dataset variant rejects" -> "none
of the narrower comparison scopes tested in Script 32
rejects" with explicit enumeration (Firm A pooled alone;
Firms B+C+D pooled; all non-Firm-A pooled).
- "Three independent feature-derived scores" -> "Three
feature-derived scores ... not statistically independent".
- Contribution 4 "not at narrower scopes" -> "not in the
narrower comparison scopes tested".
- Contribution 8 "demonstrating pipeline reproducibility at
multiple scopes" -> narrowed to "K=3 + box-rule
rank-convergence reproduces at full n=686; does not
re-validate operational thresholds / LOOO / five-way / pixel
identity at the broader scope".
- "external validation" softened to "annotation-free
validation" in methodological-safeguards paragraph.
- "(5)–(8)" pipeline stage list updated with corrected
section references.
- "Published box rule" -> "inherited Paper A box rule".
- Added Big-4 pixel-identity per-firm breakdown (145/8/107/2)
in §I body for completeness.
§II Related Work:
- Replaced placeholder with explicit defer-to-master statement:
v3.20.0 §II is inherited substantively unchanged in the master
manuscript; only the LOOO addition is reproduced here.
- "[add citation]" replaced with placeholder references
[42] Stone 1974, [43] Geisser 1975, [44] Vehtari et al. 2017
explicitly marked as draft references to be finalised at
copy-edit time.
- LOOO addition reframed: composition-sensitivity band on the
mixture characterisation, not on the operational classifier.
§V Discussion:
- §V-B "v4.0 inherits and confirms" softened to "v4.0 inherits
this signature-level reading and remains consistent with it
(no signature-level diagnostic was newly run in v4)".
- §V-B "some CPAs are templated, some are hand-leaning, some
are mixed" rewritten as component-membership wording: "some
CPAs' observed signatures place their per-CPA means in the
templated/mixed/hand-leaning region of the descriptor plane".
- §V-B within-CPA unimodality explanation softened from
"produces" to "can be jointly consistent" with explicit
§III-G cross-ref.
- §V-C Firm A byte-level provenance: 145 pixel-identical
signatures verified in Script 40; 50 partners / 35 cross-year
explicitly inherited from v3 / Script 28 not regenerated in
v4 spikes.
- §V-C "anchors §IV-H's positive-anchor miss-rate" -> "is the
largest of the four Big-4 subsets, with full anchor pooling
Firm A 145, Firm B 8, Firm C 107, Firm D 2".
- §V-E "published box rule" -> "inherited Paper A box rule";
"produce the same per-CPA ranking" -> "broadly concordant
rankings, with residual non-Firm-A disagreement".
- §V-G limitations expanded from 7 to 12 items: restored the
5 v3.20.0 inherited limitations (transferred ImageNet
features, HSV stamp-removal artifacts, longitudinal scan
confounds, source-exemplar misattribution, legal
interpretation).
- §V-G scope limitation: removed unsupported "narrower or
broader scopes" full-dataset dip-test claim.
§VI Conclusion:
- Names operational output: "inherited Paper A five-way
per-signature classifier with worst-case document-level
aggregation".
- "Cross-scope pipeline reproducibility" -> "K=3 + box-rule
rank-convergence reproduces at full n=686; does not
re-validate operational thresholds, LOOO, five-way classifier,
or pixel-identity at the broader scope".
- Future-work direction 3 explicitly qualifies the within-Big-4
contrast as "accountant-level descriptive features of the K=3
mixture, not validated mechanism-level claims and not
currently linked to audit-quality outcomes".
Round 26 closure post-v2:
- All 9 Major findings: CLOSED in v2 prose body.
- All 12 Minor findings: CLOSED in v2 prose body.
- Phase 5 readiness: should now move from Partial to Yes
pending codex round 27 verification.
Provenance: codex round-26 confirmed 17/17 numerical claims in
Phase 4 v1 (only finding #5, the scope-test wording, was an
overclaim rather than a numerical error). v2 keeps all confirmed
numerics and narrows only the scope-test wording.
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.