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# ASL Recognition Model |
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This project provides an open-source implementation of an American Sign Language (ASL) Recognition Model. The model leverages machine learning and computer vision techniques to recognize ASL hand signs from images. |
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## Features |
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- **Hand Landmark Detection**: Utilizes MediaPipe to accurately detect 21 hand landmarks in images. |
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- **Feature Extraction**: Calculates angles between all pairs of landmarks to form a 420-dimensional feature vector. |
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- **Vector Calculation**: Computes vectors between each pair of landmarks. |
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- **Angle Computation**: Uses the arccosine of normalized vector components to derive angles. |
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- **Model Input**: The extracted angles serve as input features for the Random Forest model, which classifies the ASL sign. |
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## Technical Stack |
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- **Python**: Core programming language. |
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- **OpenCV**: For image processing and manipulation. |
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- **MediaPipe**: For detecting hand landmarks. |
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- **Scikit-learn**: Provides the Random Forest model for classification. |
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- **Streamlit**: Facilitates an interactive user interface for real-time recognition. |
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## Supported Alphabets |
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The model currently works for the following ASL alphabets: |
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- A, B, C, E, F, G, H, I, J, K, L, O, Q, R, S, W, Y |
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The model does not support or may not work correctly for: |
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- D, M, N, P, T, U, V, X, Z |
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## Usage |
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1. Upload an image of an ASL sign through the Streamlit interface. |
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2. The model processes the image and provides the top 5 predictions along with visualizations of detected hand landmarks. |
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## Contribution |
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We welcome contributions to improve the model's accuracy and expand its alphabet coverage. Feel free to fork the repository, submit issues, or create pull requests. |
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## License |
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This project is open-source and available under the [MIT License](LICENSE). |
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## Acknowledgments |
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Thanks to the contributors of MediaPipe and Scikit-learn for their powerful libraries that made this project possible. |