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Official implementation of Character Region Awareness for Text Detection (CRAFT)

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CRAFT Text Detection ONNX Export & Inference 🚀

This repository contains code to export CRAFT (Character Region Awareness For Text Detection) model to ONNX format and run inference.

💎 Support

If you find this tool useful, consider becoming a sponsor for $1/month and get priority support!


Prerequisites 📋

  • Docker 🐳
  • High-resolution text images 🖼️

Directory Structure 📁


├── data/ # Add your high-res images here
├── onnx/ # ONNX model will be exported here
├── weights/ # craft_mlt_25k.pth model goes here
├── outputs/ # Detection results will be saved here
├── Dockerfile
├── onnx-export.py
└── onnx-inference.py

Quick Start 🏃‍♂️

  1. Add Images and download pth file 📸

    • Place your high-resolution text images in the data directory
    • Download pytorch model from here, and save it in weights folder
  2. Build Docker Image 🔨

    docker build -t craft-onnx:latest .
  3. Run Docker Container 🐋

    docker run -it craft-onnx:latest /bin/bash
  4. Export ONNX Model 📤

    python3 onnx-export.py

    This will:

    • Take a sample image from data directory
    • Export ONNX graph to onnx folder
  5. Run Inference 🔍

    python3 onnx-inference.py
    • Uses the exported ONNX model
    • Saves detection results in outputs directory

Model Details ⚙️

  • Input size: 1280x960
  • Optimized for high-resolution document images
  • CPU-friendly inference

Notes 📝

  • Make sure images are readable and have sufficient resolution
  • The model works best with clear, well-lit document images
  • Check outputs directory for detection results

💎 Sponsor $1 per month

👉 My GitHub Sponsors link

🌟 Sponsor Benefits

  • 🏢 Priority support for integrating this tool into your company's infrastructure
  • 🛠️ Direct assistance with project-related issues and customizations
  • 💡 Technical consultation for your specific use cases
  • 🚀 Early access to new features and improvements
  • ⭐ Recognition in our sponsors list

Why Sponsor?

Your sponsorship helps me maintain the code, ensuring it remains a robust and reliable tool for the community. Every contribution, no matter how small, makes a difference!

License 📄

MIT License

This Repository

This repository's modifications and additions are licensed under MIT License.

Third-Party Licenses

Acknowledgments 🙏

This work builds upon:

  • CRAFT-pytorch by CLOVA AI Research
  • Other open source projects listed in requirements.txt

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