Add hybrid signature extraction with name-based verification

Implement VLM name extraction + CV detection hybrid approach to
replace unreliable VLM coordinate system with name-based verification.

Key Features:
- VLM extracts signature names (周寶蓮, 魏興海, etc.)
- CV or PDF text layer detects regions
- VLM verifies each region against expected names
- Signatures saved with person names: signature_周寶蓮.png
- Duplicate prevention and rejection handling

Test Results:
- 5 PDF pages tested
- 7/10 signatures extracted (70% recall)
- 100% precision (no false positives)
- No blank regions extracted (previous issue resolved)

Files:
- extract_pages_from_csv.py: Extract pages from CSV (tested: 100 files)
- extract_signatures_hybrid.py: Hybrid extraction (current working solution)
- extract_handwriting.py: CV-only approach (component)
- extract_signatures_vlm.py: Deprecated VLM coordinate approach
- PROJECT_DOCUMENTATION.md: Complete project history and results
- SESSION_INIT.md: Session handoff documentation
- SESSION_CHECKLIST.md: Status checklist
- NEW_SESSION_PROMPT.txt: Template for next session
- HOW_TO_CONTINUE.txt: Visual handoff guide
- COMMIT_SUMMARY.md: Commit preparation guide
- README.md: Quick start guide
- README_page_extraction.md: Page extraction docs
- README_hybrid_extraction.md: Hybrid approach docs
- .gitignore: Exclude diagnostic scripts and outputs

Known Limitations:
- 30% of signatures missed due to conservative CV parameters
- Text layer method untested (all test PDFs are scanned images)
- Performance: ~24 seconds per PDF

Next Steps:
- Tune CV parameters for higher recall
- Test with larger dataset (100+ files)
- Process full dataset (86,073 files)

🤖 Generated with Claude Code

README.md

@@ -0,0 +1,72 @@
+# 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
+```bash
+cd /Volumes/NV2/pdf_recognize
+source venv/bin/activate
+python extract_pages_from_csv.py
+```
+
+### Step 2: Extract Signatures
+```bash
+python extract_signatures_hybrid.py
+```
+
+## Documentation
+
+- **[PROJECT_DOCUMENTATION.md](PROJECT_DOCUMENTATION.md)** - Complete project history, all approaches tested, detailed results
+- **[README_page_extraction.md](README_page_extraction.md)** - Page extraction documentation
+- **[README_hybrid_extraction.md](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](PROJECT_DOCUMENTATION.md) for complete details.