---
title: eDOCr2 - Engineering Drawing OCR
emoji: 🔧
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

# 🔧 eDOCr2 - Engineering Drawing OCR

Extract **dimensions**, **tables**, and **GD&T symbols** from engineering drawings automatically using deep learning.

## 🎯 Features

- ✅ **Table Extraction** - Title blocks, revision tables, bill of materials
- ✅ **GD&T Recognition** - Geometric dimensioning and tolerancing symbols
- ✅ **Dimension Detection** - Measurements with tolerances
- ✅ **Multi-format Support** - JPG, PNG, PDF
- ✅ **Structured Output** - JSON and CSV export
- ✅ **Visual Annotation** - Highlighted detection results

## 🚀 How to Use

1. **Upload** your engineering drawing (JPG, PNG, or PDF)
2. **Click** "Process Drawing" button
3. **View** annotated results and extracted data
4. **Download** complete results as ZIP file

## 📊 What Gets Extracted

### Tables
- Title blocks with part information
- Revision history tables
- Bill of materials (BOM)
- General notes and specifications

### GD&T Symbols
- Geometric tolerancing symbols
- Feature control frames
- Datum references

### Dimensions
- Linear dimensions
- Angular dimensions
- Tolerance values
- Diameter and radius callouts

## 🔧 Technology Stack

- **Deep Learning Models**: Custom-trained Keras OCR models
- **Text Detection**: CRAFT-based detector
- **Text Recognition**: CRNN-based recognizer
- **Symbol Matching**: Template matching algorithms
- **Framework**: Gradio for web interface

## 📚 Research

This tool is based on the research paper:

**"eDOCr2: Automated Extraction of Information from Engineering Drawings"**  
[http://dx.doi.org/10.2139/ssrn.5045921](http://dx.doi.org/10.2139/ssrn.5045921)

## 💡 Tips for Best Results

- Use **high-resolution** scans (300 DPI or higher)
- Ensure **clear text** and symbols
- Avoid **skewed** or rotated images
- Use **clean** drawings without handwritten annotations

## 🛠️ Local Installation

To run this locally:

```bash
# Clone repository
git clone https://github.com/javvi51/edocr2.git
cd edocr2

# Install dependencies
pip install -r requirements.txt

# Download models (see releases)
# Place in edocr2/models/

# Run app
python app.py
```

## 📦 Model Files

The pre-trained models are automatically loaded from the repository:
- `recognizer_gdts.keras` (67.2 MB) - GD&T symbol recognition
- `recognizer_dimensions_2.keras` (67.2 MB) - Dimension recognition

Download from: [GitHub Releases](https://github.com/javvi51/edocr2/releases/tag/v1.0.0)

## 🔗 Links

- **GitHub Repository**: [github.com/javvi51/edocr2](https://github.com/javvi51/edocr2)
- **Research Paper**: [DOI:10.2139/ssrn.5045921](http://dx.doi.org/10.2139/ssrn.5045921)
- **Original Author**: Javier Villena Toro
- **Deployed by**: Jeyanthan GJ

## 📝 License

MIT License - See LICENSE file for details

## 🤝 Citation

If you use this tool in your research, please cite:

```bibtex
@article{villena2024edocr2,
  title={eDOCr2: Automated Extraction of Information from Engineering Drawings},
  author={Villena Toro, Javier},
  year={2024},
  doi={10.2139/ssrn.5045921}
}
```

## ⚠️ Limitations

- Works best with mechanical/production drawings
- Requires clear, high-quality scans
- May struggle with handwritten annotations
- Processing time: 10-30 seconds per drawing

## 🐛 Known Issues

- PDF support limited to first page only
- Very large images (>10MB) may timeout
- Some custom GD&T symbols may not be recognized

## 📧 Contact

For issues and questions:
- Open an issue on [GitHub](https://github.com/javvi51/edocr2/issues)
- Check the [documentation](https://github.com/javvi51/edocr2/blob/main/docs/examples.md)

---

**Enjoy using eDOCr2! 🚀**