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| title: Gregg Shorthand Recognition | |
| emoji: 🖋️ | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 4.20.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| tags: | |
| - gregg-shorthand | |
| - handwriting-recognition | |
| - ocr | |
| - stenography | |
| - historical-documents | |
| - computer-vision | |
| - pytorch | |
| models: | |
| - a0a7/gregg-recognition | |
| # Gregg Shorthand Recognition Space | |
| This is an interactive demo for recognizing Gregg shorthand notation from images. | |
| ## How to Use | |
| Upload an image containing Gregg shorthand notation and submit | |
| ## Model Information | |
| This demo uses the Gregg Recognition model trained specifically for Gregg shorthand notation. The model combines: | |
| - Convolutional Neural Networks (CNN) for feature extraction | |
| - Long Short-Term Memory (LSTM) networks for sequence modeling | |
| - Advanced pattern recognition techniques | |
| - Specialized preprocessing for shorthand symbols | |
| ## Technical Details | |
| - **Model Type**: Image-to-Text Recognition | |
| - **Architecture**: CNN-LSTM with Pattern Database | |
| - **Input Size**: 256x256 pixels | |
| - **Framework**: PyTorch | |
| - **Preprocessing**: Grayscale conversion, normalization | |
| ## Repository | |
| Source code and model details: [GitHub Repository](https://github.com/a0a7/GreggRecognition) | |