166 lines
4.4 KiB
Markdown
166 lines
4.4 KiB
Markdown
# PaddleOCRVL Integration Guide
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## Overview
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`test_accuracy_batch_full.py` now supports two OCR models for seal text recognition:
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1. **PP-OCRv5_server_rec** (default) - Traditional OCR model
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2. **PaddleOCRVL** - Vision-Language model with superior accuracy
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## Usage
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### Option 1: Command Line Arguments
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```bash
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# Use default PP-OCRv5 model
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python test_accuracy_batch_full.py
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# Use PaddleOCRVL model (recommended for better accuracy)
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python test_accuracy_batch_full.py --ocr-model paddleocr_vl
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# Process specific number of PDFs
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python test_accuracy_batch_full.py --batch-size 5 --ocr-model paddleocr_vl
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```
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### Option 2: Environment Variable
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```bash
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# Set environment variable
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export OCR_MODEL=paddleocr_vl # Linux/Mac
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set OCR_MODEL=paddleocr_vl # Windows
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# Run script (will use environment variable)
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python test_accuracy_batch_full.py
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```
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## Performance Comparison
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Based on WTS2025-21283.pdf test:
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| Model | Recognized Text | Accuracy | Score |
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|-------|----------------|----------|-------|
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| PP-OCRv5_server_rec | 械检测技术有限公司 | 84.2% | 0.8291 |
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| **PaddleOCRVL** | **威凯检测技术有限公司** | **100%** ✅ | N/A |
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## Requirements
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For PaddleOCRVL, ensure you have:
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```bash
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pip install paddleocr[doc-parser]
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pip install paddlepaddle==3.2.0 # Use 3.2.0, not 3.3.0
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```
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## API Usage
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### In your own code:
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```python
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from paddleocr import PaddleOCRVL
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import json
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# Initialize PaddleOCRVL with seal recognition
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pipeline = PaddleOCRVL(
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use_seal_recognition=True,
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use_ocr_for_image_block=True,
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use_layout_detection=True
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)
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# Run prediction on unwarp seal image
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output = pipeline.predict("seal_unwarp_0.png")
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# Extract seal text from result
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result = output[0]
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result.save_to_json(save_path="output")
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# Read JSON to get seal text
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with open("output/seal_unwarp_0_res.json", 'r', encoding='utf-8') as f:
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data = json.load(f)
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for block in data['parsing_res_list']:
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if block['block_label'] == 'seal':
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seal_text = block['block_content']
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print(f"Seal text: {seal_text}")
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```
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## Implementation Details
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### Modified Functions
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1. **`run_ocr_recognition_vl()`** - New function for PaddleOCRVL recognition
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- Saves temp JSON files
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- Extracts `block_content` from `seal` blocks
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- Returns standardized result format
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2. **`extract_seals_and_institutions()`** - Enhanced with OCR model selection
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- Added `ocr_model` parameter ("ppocr_v5" or "paddleocr_vl")
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- Added `vl_pipeline` parameter for PaddleOCRVL instance
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- Automatic fallback to PP-OCRv5 if PaddleOCRVL unavailable
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3. **`process_single_pdf()`** - Updated to pass OCR model parameters
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4. **`main()`** - Added command line argument parsing
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### Key Configuration
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```python
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# In test_accuracy_batch_full.py
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# OCR Model Selection (via environment variable or command line)
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OCR_MODEL = os.environ.get("OCR_MODEL", "ppocr_v5")
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# Check PaddleOCRVL availability
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try:
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from paddleocr import PaddleOCRVL
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PADDLEOCRVL_AVAILABLE = True
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except ImportError:
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PADDLEOCRVL_AVAILABLE = False
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```
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## Troubleshooting
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### Issue: "PaddleOCRVL not available"
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**Solution:**
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```bash
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pip install paddleocr[doc-parser]
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```
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### Issue: "use_seal_recognition or use_ocr_for_image_block not enabled"
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**Solution:** Make sure to initialize with correct parameters:
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```python
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pipeline = PaddleOCRVL(
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use_seal_recognition=True, # Required!
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use_ocr_for_image_block=True # Required!
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)
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```
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### Issue: PaddlePaddle 3.3.0 compatibility error
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**Solution:** Downgrade to 3.2.0:
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```bash
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pip install paddlepaddle==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
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```
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## File Structure
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```
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test_accuracy_batch_full.py
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├── run_ocr_recognition() # PP-OCRv5 recognition (existing)
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├── run_ocr_recognition_vl() # PaddleOCRVL recognition (new)
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├── extract_seals_and_institutions() # Enhanced with model selection
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└── main() # Added CLI argument parsing
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```
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## Recommendations
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1. **For production use**: Use PaddleOCRVL for better accuracy
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2. **For testing/debugging**: Use PP-OCRv5 for faster iteration
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3. **For batch processing**: PaddleOCRVL is slower but more accurate
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## Next Steps
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- [ ] Run full batch test with PaddleOCRVL on all PDFs
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- [ ] Compare accuracy metrics between models
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- [ ] Benchmark processing time for both models
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- [ ] Consider adding hybrid approach (try PP-OCRv5 first, fallback to PaddleOCRVL on low confidence)
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