- name
- rapid-ocr-reader
- version
- 1.0.0
- owner
- rendaixue-byte
- description
- Extract text from images using OCR (Optical Character Recognition). Use this skill when you need to read text content from images, screenshots, photos, or any image file. Supports Chinese and English text with high accuracy. Triggers when user sends an image and asks to read/extract/identify text from it, or when you receive an image that contains text you need to understand.
- license
- MIT
Image Reader - OCR Text Extraction
A high-performance OCR skill for extracting text from images. Powered by RapidOCR with PP-OCRv4 models, supporting Chinese and English text recognition.
Features
- Multi-language: Chinese (simplified/traditional), English, and mixed text
- High accuracy: PP-OCRv4 model with >95% accuracy on typical screenshots
- Structured output: Text with confidence scores and bounding boxes
- Image info: Dimensions, format, and color mode included
- Fast: CPU-only, no GPU required
Quick Start
python scripts/read_image.py /path/to/image.jpgUsage Examples
Extract text from a screenshot
python scripts/read_image.py screenshot.pngJSON Output
The script outputs structured JSON:
{
"success": true,
"text": "Full extracted text",
"lines": [
{
"text": "Individual line",
"confidence": 0.98,
"box": [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
}
],
"line_count": 5,
"image_info": {
"format": "PNG",
"size": [1920, 1080],
"mode": "RGB"
}
}Requirements
pip install rapidocr onnxruntime pillowFirst run will download OCR models (~50MB) automatically.
Common Use Cases
- UI Screenshots: Extract text from app/website screenshots
- Document Photos: Read text from photographed documents
- Diagrams: Extract labels and annotations
- Receipts: Parse receipt/invoice data
Output Fields
| Field | Type | Description |
|---|---|---|
| success | bool | Whether OCR succeeded |
| text | string | All extracted text |
| lines | array | Individual text lines with metadata |
| line_count | int | Number of text lines detected |
| image_info | object | Image metadata |
Technical Details
- Engine: RapidOCR (ONNX Runtime backend)
- Models: PP-OCRv4 (detection + recognition)
- Languages: Chinese, English (auto-detected)
- Performance: ~1-2 seconds per image on CPU
License
MIT License
Third-party dependencies:
- RapidOCR - Apache 2.0 License
- ONNX Runtime - MIT License