gbanyan
52612e14ba
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
144 lines
3.5 KiB
Markdown
144 lines
3.5 KiB
Markdown
# PDF Page Extraction Script
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This script extracts specific PDF pages listed in `master_signatures.csv`.
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## What It Does
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**Simple page extraction - NO image detection:**
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1. Reads the CSV file with filename and page number
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2. Finds the PDF file in batch directories
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3. Extracts the specified page
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4. Saves it as a single-page PDF
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**No filtering** - extracts all pages listed in the CSV regardless of content.
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## Configuration
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Edit these values in `extract_pages_from_csv.py`:
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```python
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CSV_PATH = "/Volumes/NV2/PDF-Processing/master_signatures.csv"
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PDF_BASE_PATH = "/Volumes/NV2/PDF-Processing/total-pdf"
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OUTPUT_PATH = "/Volumes/NV2/PDF-Processing/signature-image-output"
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TEST_LIMIT = 100 # Number of rows to process from CSV
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```
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## Usage
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### Test with 100 files (current setting)
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```bash
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cd /Volumes/NV2/pdf_recognize
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source venv/bin/activate
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python extract_pages_from_csv.py
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```
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### Process all files in CSV
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Edit line 16 in `extract_pages_from_csv.py`:
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```python
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TEST_LIMIT = None # Process all rows
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```
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Or set a specific number:
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```python
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TEST_LIMIT = 1000 # Process first 1000 rows
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```
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## Input Format
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CSV file must have these columns:
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- `source_folder` - Original folder name
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- `source_subfolder` - Subfolder name
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- `filename` - PDF filename
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- `page` - Page number to extract (1-indexed)
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Example:
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```csv
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source_folder,source_subfolder,filename,page
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Ai1,01,201301_1324_AI1.pdf,3
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Ai1,01,201301_2061_AI1.pdf,5
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```
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## Output
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### Extracted PDFs
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Location: `/Volumes/NV2/PDF-Processing/signature-image-output/`
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**Naming:** `{original_filename}_page{page_number}.pdf`
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Examples:
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- `201301_1324_AI1_page3.pdf` - Page 3 from original
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- `201302_4915_AI1_page4.pdf` - Page 4 from original
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### Log File
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Location: `/Volumes/NV2/PDF-Processing/signature-image-output/page_extraction_log_YYYYMMDD_HHMMSS.csv`
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Columns:
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- `source_folder` - From CSV
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- `source_subfolder` - From CSV
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- `filename` - PDF filename
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- `page` - Page number
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- `pdf_found` - True/False if PDF was found
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- `exported` - True/False if page was extracted
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- `error_message` - Error details if any
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## How It Works
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```python
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# 1. Find PDF in batch directories
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pdf_path = find_pdf_file(filename)
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# 2. Open PDF and extract specific page
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doc = fitz.open(pdf_path)
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output_doc = fitz.open()
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output_doc.insert_pdf(doc, from_page=page-1, to_page=page-1)
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# 3. Save extracted page
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output_doc.save(output_path)
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```
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**Key points:**
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- ✅ Simple and fast - no image analysis
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- ✅ Extracts exactly what's in the CSV
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- ✅ Handles missing PDFs gracefully
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- ✅ Validates page numbers
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- ✅ Detailed logging for troubleshooting
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## Directory Structure
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```
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/Volumes/NV2/PDF-Processing/
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├── master_signatures.csv # Input CSV
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├── total-pdf/ # Source PDFs
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│ ├── batch_01/
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│ ├── batch_02/
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│ └── ...
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└── signature-image-output/ # Output directory
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├── page_extraction_log_*.csv # Processing log
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└── *_page*.pdf # Extracted pages
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```
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## Performance
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- Processing speed: ~1-2 files per second
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- 100 files: ~1-2 minutes
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- Full dataset (86,073 files): ~12-24 hours estimated
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## Error Handling
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The script handles:
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- ✅ PDF file not found in batch directories
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- ✅ Invalid page numbers (beyond PDF page count)
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- ✅ Corrupt or unreadable PDFs
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- ✅ File system errors
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All errors are logged in the CSV log file.
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## Next Steps
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After extracting pages, use `extract_handwriting.py` to detect and extract handwritten regions from the extracted pages.
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## Dependencies
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- Python 3.9+
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- PyMuPDF (fitz) - Installed in venv
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