技能备份 - 2026-04-15 (40个技能)
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name: super-ocr
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description: "Production-grade OCR with intelligent engine selection. Tesseract (lightweight, fast) and PaddleOCR (high accuracy, Chinese-optimized). Use when extracting text from images, processing Chinese documents, needing confidence scores, or working with mixed Chinese/English content."
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# Super OCR
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## Overview
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Super OCR is a production-grade optical character recognition tool that intelligently selects the best engine for your needs:
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- **Tesseract Engine**: Lightweight, fast (~200-500ms), perfect for simple text extraction
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- **PaddleOCR Engine**: High accuracy (98%+), optimized for Chinese, ideal for complex documents
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## Engine Selection Strategy
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### Auto Mode (Default)
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The skill automatically selects the optimal engine:
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| Scenario | Selected Engine | Why |
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|----------|----------------|-----|
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| Simple text, English only | Tesseract | Faster, lighter dependency |
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| Chinese content, high accuracy needed | PaddleOCR | Better Chinese support, 98%+ accuracy |
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| Low confidence from Tesseract | PaddleOCR (fallback) | Quality assurance |
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### Force Mode
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Users can explicitly choose an engine:
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- `--engine tesseract` - Use Tesseract only
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- `--engine paddle` - Use PaddleOCR only
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- `--engine auto` - Auto-select (default)
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## Quick Start
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### Installation
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This skill requires the following dependencies:
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- **PaddleOCR** (for Chinese text recognition - 98%+ accuracy)
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- **Tesseract** (for fast English text recognition)
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- **OpenCV** (for image preprocessing)
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#### Option 1: Install with pip (all-in-one)
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```bash
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pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy
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```
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#### Option 2: Install dependencies manually
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**macOS:**
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```bash
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# Tesseract
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brew install tesseract
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# PaddleOCR
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pip install paddleocr paddlepaddle
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```
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**Ubuntu/Debian:**
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```bash
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# Tesseract
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sudo apt update && sudo apt install tesseract-ocr
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# PaddleOCR
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pip install paddleocr paddlepaddle
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```
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**Windows:**
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```bash
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# Download Tesseract from: https://github.com/UB-Mannheim/tesseract/wiki
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pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy
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```
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### Usage
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```bash
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# Auto mode (recommended) - runs all available engines
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cd path/to/super-ocr
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python scripts/main.py --image path/to/image.png
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# Force Tesseract only
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python scripts/main.py --image document.jpg --engine tesseract
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# Force PaddleOCR (high accuracy Chinese)
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python scripts/main.py --image chinese_menu.png --engine paddle
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# Run all engines (macOS only: Tesseract + PaddleOCR + MacVision)
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python scripts/main.py --image complex_doc.png --engine all
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# Batch processing with output directory
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python scripts/main.py --images ./images/*.png --output ./results --verbose
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# Check dependencies and auto-install
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python scripts/dependencies.py --check --install
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```
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## Structuring This Skill
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This skill uses a **capabilities-based** structure with multiple execution modes:
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1. **Engine Selection Logic** - Intelligent decision making
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2. **OCR Execution** - Unified interface for different engines
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3. **Post-processing** - Standardized output formatting
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4. **Validation & Fallback** - Quality assurance
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## Core Capabilities
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### 1. Intelligent Engine Selection
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The skill includes a decision tree that analyzes:
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- Image characteristics (contrast, text size)
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- Language patterns (Chinese character detection)
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- User requirements (speed vs accuracy)
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See `scripts/engine_selector.py` for implementation details.
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### 2. Dual Engine Support
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**Tesseract Engine** (`scripts/tesseract_ocr.py`):
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- Fast preprocessing pipeline
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- PSM mode 6 for uniform text blocks
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- Confidence scoring per word
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- Language detection
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**PaddleOCR Engine** (`scripts/paddle_ocr.py`):
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- State-of-art? SN (East text detection)
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- Crnn recognition with LSTM
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- Confidence scores per character
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- Table detection support
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### 3. Output Formats
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Supports multiple output formats:
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| Format | Content | Use Case |
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|--------|---------|----------|
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| Text only | Clean extracted text | Simple search/grep |
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| Structured | Text + positions | Data extraction |
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| JSON | Full metadata + confidence | API integration |
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| Verbose | Debug info | Quality assurance |
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### 4. Quality Guarantees
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- Confidence thresholds (configurable, default 80%)
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- Low-confidence alerts for manual review
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- \Fallback processing for failed OCRs
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## Resources
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### scripts/
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- `main.py` - Main entry point, CLI interface (supports multi-engine)
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- `dependencies.py` - Auto-install and validation
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- `output_formatter.py` - Multiple output format support
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- `engine/` - OCR engine implementations
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- `selector.py` - Intelligent engine selection logic
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- `tesseract.py` - Tesseract engine wrapper
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- `paddle.py` - PaddleOCR engine wrapper
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- `macvision.py` - macOS Vision OCR (macOS only)
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- `preprocessing/` - Image preprocessing utilities
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- `preprocessor.py` - Denoising, enhancement, binarization
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### dependencies.py (Key Feature)
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The `dependencies.py` module handles:
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- Dependency detection (`paddleocr`, `paddlepaddle`, `pytesseract`, `cv2`)
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- Auto-install on missing dependencies
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- version checking
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- OS-specific installation commands
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- Clear error messages with troubleshooting steps
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Use this when setting up a new environment with `python scripts/dependencies.py --check --install`
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## Advanced Features
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### Custom Configuration
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Create `config.yaml` for persistent settings:
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```yaml
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default_engine: auto
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confidence_threshold: 0.8
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output_format: json
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preprocess:
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denoise: true
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enhance_contrast: true
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```
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### Batch Processing
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Process multiple images:
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```bash
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python scripts/ocr.py --images ./images/*.png --output ./results
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```
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### API Mode
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Use as a Python library:
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```python
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from super_ocr import OCRProcessor
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processor = OCRProcessor(engine='auto')
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result = processor.extract('image.png')
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print(result.text)
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print(result.confidence)
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```
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## Anti-Patterns
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- ❌ Using PaddleOCR for every image (overhead for simple cases)
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- ❌ ignoring confidence scores (quality matters)
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- ❌ Biases (always prefering one engine)
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- ❌ Skipping preprocessing (quality impact)
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## Performance Notes
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| Engine | Init Time | Per-Image | Memory | Best For |
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|--------|-----------|-----------|----------|----------|
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| Tesseract | ~200ms | ~50ms | ~100MB | Quick extraction |
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| PaddleOCR | ~3s | ~500ms | ~500MB | High accuracy |
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Initialize once, reuse processor for batch processing.
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