An intelligent OCR integration for Openclaw Skills that automatically selects between Tesseract and PaddleOCR based on document complexity and language.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install super-ocr
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install super-ocr using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Super OCR?
Super OCR is a production-grade optical character recognition tool designed to integrate seamlessly with Openclaw Skills. It solves the common trade-off between speed and accuracy by providing an intelligent selection layer that chooses the best engine for the task. Whether you need the lightweight speed of Tesseract for simple English text or the deep-learning precision of PaddleOCR for complex Chinese documents, this skill manages the complexity for you.
By adding Super OCR to your collection of Openclaw Skills, you gain the ability to process mixed-language documents with over 98% accuracy. The system includes built-in image preprocessing via OpenCV to ensure high-quality inputs, and it provides detailed confidence scores to guarantee the reliability of extracted data for downstream AI agent workflows.
Super OCR Use Cases
- High-accuracy extraction of Chinese and English text from complex documents.
- Automated data entry workflows powered by Openclaw Skills.
- Rapid batch processing of images for search indexing.
- Fallback OCR logic for mission-critical document processing.
- Integrating OCR capabilities into custom AI agent scripts.
How Super OCR Works
- Image Analysis: The skill analyzes the input image for language markers and visual complexity.
- Engine Selection: Based on the analysis or user flags, it selects Tesseract, PaddleOCR, or the macOS Vision engine.
- Preprocessing: OpenCV applies denoising, contrast enhancement, and binarization to improve recognition rates.
- OCR Execution: The selected engine processes the image and generates text with positional metadata.
- Validation: The system checks confidence scores and triggers fallbacks if quality thresholds are not met.
Super OCR Setup
Ensure you have the necessary system dependencies installed before integrating this with your Openclaw Skills environment.
# Install Python dependencies
pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy
# Ubuntu/Debian Tesseract installation
sudo apt update && sudo apt install tesseract-ocr
# Verify and auto-configure dependencies
python scripts/dependencies.py --check --install
Super OCR Data Schema & Taxonomy
The skill produces structured data that can be consumed by other Openclaw Skills.
| Format | Description | Content |
|---|---|---|
| Text Only | Simple string output | Clean extracted text for logs or search |
| Structured | Text + Geometry | Bounding boxes and positional data |
| JSON | Full Metadata | Comprehensive output including engine used and confidence scores |
Super OCR Advanced Features
- Auto-Selection Logic: Uses an intelligent decision tree to balance performance and accuracy requirements.
- Multi-Engine Support: Unified interface for Tesseract, PaddleOCR, and macOS-native Vision OCR.
- Batch Processing: Optimized for processing entire directories of images within Openclaw Skills automation pipelines.
- API & Library Mode: Support for importing the processor directly into Python projects as a modular library.
- Custom Configuration: Fine-tune confidence thresholds and preprocessing steps via a persistent config.yaml file.
SKILL.md
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