gbanyan
12f716ddf1
Fully addresses the partial-resolution / unfixed items from codex
gpt-5.4 round-4 review (codex_review_gpt54_v3_4.md):
Critical
- Table XI z/p columns now reproduce from displayed counts. Earlier
table had 1-4-unit transcription errors in k values and a fabricated
cos > 0.9407 calibration row; both fixed by rerunning Script 24
with cos = 0.9407 added to COS_RULES and copying exact values from
the JSON output.
- Section III-L classifier now defined entirely in terms of the
independent-minimum dHash statistic that the deployed code (Scripts
21, 23, 24) actually uses; the legacy "cosine-conditional dHash"
language is removed. Tables IX, XI, XII, XVI are now arithmetically
consistent with the III-L classifier definition.
- "0.95 not calibrated to Firm A" inconsistency reconciled: Section
III-H now correctly says 0.95 is the whole-sample Firm A P95 of the
per-signature cosine distribution, matching III-L and IV-F.
Major
- Abstract trimmed to 246 words (from 367) to meet IEEE Access 250-word
limit. Removed "we break the circularity" overclaim; replaced with
"report capture rates on both folds with Wilson 95% intervals to
make fold-level variance visible".
- Conclusion mirrors the Abstract reframe: 70/30 split documents
within-firm sampling variance, not external generalization.
- Introduction no longer promises precision / F1 / EER metrics that
Methods/Results don't deliver; replaced with anchor-based capture /
FAR + Wilson CI language.
- Section III-G within-auditor-year empirical-check wording corrected:
intra-report consistency (IV-H.3) is a different test (two co-signers
on the same report, firm-level homogeneity) and is not a within-CPA
year-level mixing check; the assumption is maintained as a bounded
identification convention.
- Section III-H "two analyses fully threshold-free" corrected to "only
the partner-level ranking is threshold-free"; longitudinal-stability
uses 0.95 cutoff, intra-report uses the operational classifier.
Minor
- Impact Statement removed from export_v3.py SECTIONS list (IEEE Access
Regular Papers do not have a standalone Impact Statement). The file
itself is retained as an archived non-paper note for cover-letter /
grant-report reuse, with a clear archive header.
- All 7 previously unused references ([27] dHash, [31][32] partner-
signature mandates, [33] Taiwan partner rotation, [34] YOLO original,
[35] VLM survey, [36] Mann-Whitney) are now cited in-text:
[27] in Methodology III-E (dHash definition)
[31][32][33] in Introduction (audit-quality regulation context)
[34][35] in Methodology III-C/III-D
[36] in Results IV-C (Mann-Whitney result)
Updated Script 24 to include cos = 0.9407 in COS_RULES so Table XI's
calibration-fold P5 row is computed from the same data file as the
other rows.
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.