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README.md

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