技能备份 - 2026-04-15 (40个技能)

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# API Reference for Super OCR
This document provides complete API documentation for using Super OCR as a Python library.
## Quick Start
```python
from super_ocr import OCRProcessor
# Auto mode (recommended)
processor = OCRProcessor(engine='auto')
result = processor.extract('image.png')
print(result['text'])
print(f"Confidence: {result['confidence']:.2%}")
print(f"Engine: {result['engine']}")
```
## OCRProcessor Class
### `__init__(engine: str = 'auto', verbose: bool = False)`
Initialize the OCR processor.
**Parameters:**
- `engine` (str): 'auto', 'tesseract', or 'paddle'. Default: 'auto'
- `verbose` (bool): Enable detailed logging. Default: False
### `extract(image_path: str) -> Dict`
Extract text from a single image.
**Parameters:**
- `image_path` (str): Path to the input image
**Returns:**
```python
{
'text': str, # Extracted text
'confidence': float, # Confidence score (0.0 - 1.0)
'engine': str, # 'tesseract' or 'paddle'
'processing_time_ms': float, # Processing time in milliseconds
'error': Optional[str], # Error message if failed
'results': List[Dict], # Detailed results (PaddleOCR only)
'line_count': int # Number of lines detected
}
```
### `batch_extract(image_paths: List[str]) -> List[Dict]`
Process multiple images.
**Parameters:**
- `image_paths` (List[str]): List of image file paths
**Returns:**
- List of result dictionaries (same structure as `extract()`)
## Engine Selection
### Auto Mode (Default)
The processor automatically selects the best engine based on:
1. **Image filename heuristics:**
- `screenshot`, `snap`, `capture` → Tesseract
- `menu`, `invoice`, `certificate`, `receipt` → PaddleOCR
- Default → PaddleOCR (better accuracy)
2. **Quality fallback:**
- If Tesseract returns low confidence, PaddleOCR is used
### Force Mode
You can explicitly choose an engine:
```python
# Force Tesseract
processor = OCRProcessor(engine='tesseract')
# Force PaddleOCR
processor = OCRProcessor(engine='paddle')
```
## Preprocessing
For advanced users, you can preprocess images before OCR:
```python
from super_ocr.preprocessing import preprocess_pipeline
# Load image
import cv2
image = cv2.imread('input.png')
# Preprocess
processed = preprocess_pipeline(
image,
denoise=True,
enhance=True,
binarize=True,
deskew=True,
resize_scale=2.0
)
# Save processed image
cv2.imwrite('processed.png', processed)
```
## Output Formats
The skill supports multiple output formats:
| Format | Description |
|--------|-------------|
| `text` | Clean extracted text only |
| `json` | Full JSON with metadata |
| `structured` | Human-readable formatted output |
| `verbose` | Debug information with confidence scores |
## Configuration
You can customize behavior by creating a `config.yaml`:
```yaml
default_engine: auto
confidence_threshold: 0.8
output_format: json
preprocess:
denoise: true
enhance_contrast: true
```
## Error Handling
```python
try:
result = processor.extract('image.png')
if result.get('error'):
print(f"Error: {result['error']}")
else:
print(result['text'])
except Exception as e:
print(f"Unexpected error: {e}")
```
## CLI Usage
```bash
# Single image
python scripts/main.py --image image.png
# Multiple images
python scripts/main.py --images ./images/*.png --output ./results
# Force engine
python scripts/main.py --image doc.png --engine paddle --verbose
# Different output format
python scripts/main.py --image img.png --format text
```
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# Engine Comparison
## Tesseract vs PaddleOCR
| Feature | Tesseract | PaddleOCR |
|---------|-----------|-----------|
| **Accuracy** | 90-95% | 98%+ |
| **Chinese Support** | Good | Excellent |
| **Speed** | ~200ms init, ~50ms/img | ~3s init, ~500ms/img |
| **Memory** | ~100MB | ~500MB |
| **Dependencies** | `pytesseract + cv2 + PIL` | `paddleocr + paddlepaddle` |
| **Best For** | Quick extraction, English | High accuracy, Chinese docs |
## When to Use Which
### Use Tesseract When:
- ✅ Extracting text from screenshots
- ✅ Processing English-only documents
- ✅ Need fast OCR (-web scraping, quick验证)
- ✅ Limited memory environment
### Use PaddleOCR When:
- ✅ Processing Chinese documents
- ✅ Need high accuracy (98%+)
- ✅ Working with complex layoutstables, forms
- ✅ Critical data extraction (invoices, contracts)
## Performance Comparison
| Task | Tesseract | PaddleOCR | Improvement |
|------|-----------|-----------|-------------|
| Screenshot text | ~70ms | ~600ms | Tesseract faster |
| Chinese menu | ~200ms | ~550ms | - |
| Invoice extraction | ~180ms | ~520ms | - |
| Certificate OCR | ~250ms | ~580ms | PaddleOCR more accurate |
## Quality Comparison
### Example: Chinese Restaurant Menu
**Tesseract (confidence: 88%)**
```
北京烤鸭
宫保鸡丁
麻婆豆腐...
```
**PaddleOCR (confidence: 99%)**
```
北京烤鸭
宫保鸡丁
麻婆豆腐
...
```
### Example: English Invoice
**Tesseract (confidence: 92%)**
```
Invoice #12345
Date: 2024-03-05
Amount: $199.99
```
**PaddleOCR (confidence: 98%)**
```
Invoice #12345
Date: 2024-03-05
Amount: $199.99
```
## Recommendation
- **General use**: Auto mode (PaddleOCR by default for quality)
- **Speed-critical**: Force Tesseract
- **Chinese critical**: Force PaddleOCR
- **Production**: Auto mode with fallback to PaddleOCR for low confidence
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# Troubleshooting
Common issues and solutions for Super OCR。
## Installation Issues
### "Module not found: paddleocr"
**Solution:**
```bash
pip install paddleocr paddlepaddle
```
For macOS/Linux:
```bash
pip install paddleocr paddlepaddle
```
For Windows:
```bash
pip install paddleocr paddlepaddle
```
### "Tesseract not found"
**macOS:**
```bash
brew install tesseract
```
**Ubuntu/Debian:**
```bash
sudo apt update && sudo apt install tesseract-ocr
```
**Windows:**
Download from: https://github.com/UB-Mannheim/tesseract/wiki
## Runtime Issues
### Low Confidence Results
If OCR results have low confidence:
1. **Enable verbose mode:**
```bash
python scripts/main.py --image image.png --verbose
```
2. **Preprocess image manually:**
```python
from super_ocr.preprocessing import preprocess_pipeline
import cv2
image = cv2.imread('input.png')
processed = preprocess_pipeline(image, enhance=True, binarize=True)
cv2.imwrite('processed.png', processed)
```
3. **Force PaddleOCR for better accuracy:**
```bash
python scripts/main.py --image image.png --engine paddle
```
### Memory Issues (PaddleOCR)
PaddleOCR uses ~500MB memory。If you see memory errors:
1. **Use Tesseract instead:**
```bash
python scripts/main.py --image image.png --engine tesseract
```
2. **Process images one by one:**
```bash
for img in images/*.png; do
python scripts/main.py --image "$img" --output results/
done
```
### Batch Processing Too Slow
**Solutions:**
1. **Use Tesseract for speed:**
```bash
python scripts/main.py --images ./images/*.png --engine tesseract --output ./results/
```
2. **Process in parallel:**
```bash
# macOS/Linux
find ./images -name "*.png" -print0 | xargs -0 -P 4 -I {} python scripts/main.py --image {} --output ./results/
```
3. **Initialize processor once, reuse:**
```python
processor = OCRProcessor(engine='auto')
for image in images:
result = processor.extract(image)
# Process result
```
## Configuration Issues
### Custom Configuration Not Loading
Create `config.yaml` in skill directory:
```yaml
default_engine: auto
confidence_threshold: 0.8
output_format: json
preprocess:
denoise: true
enhance_contrast: true
```
### Output Format Not Working
Check format name:
```bash
python scripts/main.py --image image.png --format json
python scripts/main.py --image image.png --format text
python scripts/main.py --image image.png --format structured
```
## Dependency Checker
Run the checker to diagnose issues:
```bash
python scripts/dependencies.py --check --verbose
python scripts/dependencies.py --install
python scripts/dependencies.py --guide
```
## Getting Help
If you encounter issues not covered here:
1. Enable verbose mode: `--verbose`
2. Check dependency status: `python scripts/dependencies.py --check`
3. Try force engine: `--engine tesseract` or `--engine paddle`
4. Report issue with:
- Python version
- OS
- Command used
- Error message