gbanyan
21df0ff387
## 研究成果 ### PP-OCRv5 API 測試 - 成功升級到 PaddleOCR 3.3.2 (PP-OCRv5) - 理解新 API 結構和調用方式 - 驗證基礎檢測功能 ### 關鍵發現 ❌ PP-OCRv5 **沒有內建手寫分類功能** - text_type 字段是語言類型，不是手寫/印刷分類 - 仍需要 OpenCV Method 3 來分離手寫和印刷文字 ### 完整 Pipeline 對比測試 - v4 (2.7.3): 檢測 14 個文字 → 4 個候選區域 - v5 (3.3.2): 檢測 50 個文字 → 7 個候選區域 - 主簽名區域：兩個版本幾乎相同 (1150x511 vs 1144x511) ### 性能分析 優點： - v5 手寫識別準確率 +13.7% (文檔承諾) - 可能減少漏檢 缺點： - 過度檢測（印章小字等） - API 完全重寫，不兼容 - 仍無法替代 OpenCV Method 3 ### 文件 - PP_OCRV5_RESEARCH_FINDINGS.md: 完整研究報告 - signature-comparison/: v4 vs v5 對比結果 - test_results/: v5 測試輸出 - test_*_pipeline.py: 完整測試腳本 ### 建議 當前方案（v2.7.3 + OpenCV Method 3）已足夠穩定， 除非遇到大量漏檢，否則暫不升級到 v5。 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <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.