gbanyan
85cfefe49f
Codex round-8 (paper/codex_review_gpt54_v3_8.md) dissented from
Gemini's Accept and gave Minor Revision because of two real
numerical/consistency issues Gemini's round-7 review missed. This
commit fixes both.
Table XV per-year Firm A baseline-share column corrected
- All 11 yearly values resynced to the authoritative
reports/partner_ranking/partner_ranking_report.md (per-year
Deloitte baseline share column):
2013: 26.2% -> 32.4% (largest error; codex's test case)
2014: 27.1% -> 27.8%
2015: 27.2% -> 27.7%
2016: 27.4% -> 26.2%
2017: 27.9% -> 27.2%
2018: 28.1% -> 26.5%
2019: 28.2% -> 27.0%
2020: 28.3% -> 27.7%
2021: 28.4% -> 28.7%
2022: 28.5% -> 28.3%
2023: 28.5% -> 27.4%
- Codex independently verified that the prior 2013 value 26.2% was
numerically impossible because the underlying JSON places 97 Firm
A auditor-years in the 2013 top-50% bucket out of 324 total, so
the full-year baseline must be at least 97/324 = 29.9%.
- All other Table XV columns (N, Top-10% k, in top-10%, share) were
already correct and unchanged.
Broken cross-references from earlier renumbering repaired
- Methodology III-E: "ablation study (Section IV-F)" pointer
corrected to "Section IV-J"; the ablation is at Section IV-J
line 412 in the current Results, while IV-F is now "Calibration
Validation with Firm A".
- Results Table XVIII note: "per-signature best-match values in
Tables IV/VI (mean = 0.980)" is orphaned after earlier
renumbering (Table IV is all-pairs distributional statistics;
Table VI is accountant-level GMM model selection). Replaced with
an explicit pointer to "Section IV-D and visualized in Table XIII
(whole-sample Firm A best-match mean ~ 0.980)". Table XIII is
the correct container of per-signature best-match mean statistics.
All other Section IV-X cross-references in methodology / results /
discussion were spot-checked and remain correct under the current
section numbering.
With these two surgical fixes, codex's round-8 ranked items (1) and
(2) are cleared. Item (3) was the final DOCX packaging pass (author
metadata fill-in, figure rendering, reference formatting) which is
done manually at submission time and does not affect the markdown.
Deferred items remain deferred:
- Visual-inspection protocol details (codex round-5 item 4)
- General reproducibility appendix (codex round-5 item 6)
Both are defensible for first IEEE Access submission per codex
round-8 assessment, since the manuscript no longer leans on visual
inspection or BD/McCrary as decisive standalone evidence.
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.