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_page_extraction.md

@@ -0,0 +1,143 @@
+# PDF Page Extraction Script
+
+This script extracts specific PDF pages listed in `master_signatures.csv`.
+
+## What It Does
+
+**Simple page extraction - NO image detection:**
+1. Reads the CSV file with filename and page number
+2. Finds the PDF file in batch directories
+3. Extracts the specified page
+4. Saves it as a single-page PDF
+
+**No filtering** - extracts all pages listed in the CSV regardless of content.
+
+## Configuration
+
+Edit these values in `extract_pages_from_csv.py`:
+
+```python
+CSV_PATH = "/Volumes/NV2/PDF-Processing/master_signatures.csv"
+PDF_BASE_PATH = "/Volumes/NV2/PDF-Processing/total-pdf"
+OUTPUT_PATH = "/Volumes/NV2/PDF-Processing/signature-image-output"
+TEST_LIMIT = 100  # Number of rows to process from CSV
+```
+
+## Usage
+
+### Test with 100 files (current setting)
+```bash
+cd /Volumes/NV2/pdf_recognize
+source venv/bin/activate
+python extract_pages_from_csv.py
+```
+
+### Process all files in CSV
+Edit line 16 in `extract_pages_from_csv.py`:
+```python
+TEST_LIMIT = None  # Process all rows
+```
+
+Or set a specific number:
+```python
+TEST_LIMIT = 1000  # Process first 1000 rows
+```
+
+## Input Format
+
+CSV file must have these columns:
+- `source_folder` - Original folder name
+- `source_subfolder` - Subfolder name
+- `filename` - PDF filename
+- `page` - Page number to extract (1-indexed)
+
+Example:
+```csv
+source_folder,source_subfolder,filename,page
+Ai1,01,201301_1324_AI1.pdf,3
+Ai1,01,201301_2061_AI1.pdf,5
+```
+
+## Output
+
+### Extracted PDFs
+Location: `/Volumes/NV2/PDF-Processing/signature-image-output/`
+
+**Naming:** `{original_filename}_page{page_number}.pdf`
+
+Examples:
+- `201301_1324_AI1_page3.pdf` - Page 3 from original
+- `201302_4915_AI1_page4.pdf` - Page 4 from original
+
+### Log File
+Location: `/Volumes/NV2/PDF-Processing/signature-image-output/page_extraction_log_YYYYMMDD_HHMMSS.csv`
+
+Columns:
+- `source_folder` - From CSV
+- `source_subfolder` - From CSV
+- `filename` - PDF filename
+- `page` - Page number
+- `pdf_found` - True/False if PDF was found
+- `exported` - True/False if page was extracted
+- `error_message` - Error details if any
+
+## How It Works
+
+```python
+# 1. Find PDF in batch directories
+pdf_path = find_pdf_file(filename)
+
+# 2. Open PDF and extract specific page
+doc = fitz.open(pdf_path)
+output_doc = fitz.open()
+output_doc.insert_pdf(doc, from_page=page-1, to_page=page-1)
+
+# 3. Save extracted page
+output_doc.save(output_path)
+```
+
+**Key points:**
+- ✅ Simple and fast - no image analysis
+- ✅ Extracts exactly what's in the CSV
+- ✅ Handles missing PDFs gracefully
+- ✅ Validates page numbers
+- ✅ Detailed logging for troubleshooting
+
+## Directory Structure
+
+```
+/Volumes/NV2/PDF-Processing/
+├── master_signatures.csv          # Input CSV
+├── total-pdf/                     # Source PDFs
+│   ├── batch_01/
+│   ├── batch_02/
+│   └── ...
+└── signature-image-output/        # Output directory
+    ├── page_extraction_log_*.csv  # Processing log
+    └── *_page*.pdf                # Extracted pages
+```
+
+## Performance
+
+- Processing speed: ~1-2 files per second
+- 100 files: ~1-2 minutes
+- Full dataset (86,073 files): ~12-24 hours estimated
+
+## Error Handling
+
+The script handles:
+- ✅ PDF file not found in batch directories
+- ✅ Invalid page numbers (beyond PDF page count)
+- ✅ Corrupt or unreadable PDFs
+- ✅ File system errors
+
+All errors are logged in the CSV log file.
+
+## Next Steps
+
+After extracting pages, use `extract_handwriting.py` to detect and extract handwritten regions from the extracted pages.
+
+## Dependencies
+
+- Python 3.9+
+- PyMuPDF (fitz) - Installed in venv