ASL Hand Sign Recognition

This project uses MediaPipe to extract hand landmarks and a Random Forest model to recognize American Sign Language (ASL) alphabet letters. It also includes real-time sign recognition using your webcam.

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📌 Features

• Uses the ASL Alphabet dataset from Kaggle
• Extracts hand landmarks using MediaPipe
• Trains a Random Forest classifier on landmark data
• Tests accuracy on validation data
• Predicts ASL hand signs in real-time via webcam

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🛠 Requirements

Install the required Python libraries:

pip install mediapipe opencv-python scikit-learn joblib tqdm

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🚀 How to Use

📥 Step 1: Download Dataset

1. Download the ASL Alphabet Dataset from Kaggle:
   https://www.kaggle.com/datasets/grassknoted/asl-alphabet
2. Extract it and place it inside a folder named dataset in the root of this project.

Step 2 : Extract the labels from Dataset

python data_extractor.py   

Step 3: Train the Model

Run the training script:

python train.py   

This will train and save the model as asl_model.pkl.

Step 4: Test the Model

Evaluate the model:

python test.py

Step 5: Real-Time Prediction

Start real-time webcam recognition:

python predict.py

• Ensure your webcam is working.
• Show your hand clearly in front of the camera.
• Press q to quit.

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⚙️ How It Works

• MediaPipe detects 21 hand landmarks (x, y, z coordinates).
• These landmarks are used as features.
• A Random Forest classifier is trained on this feature set to predict ASL letters.
• OpenCV manages real-time webcam access and display.

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📝 Notes

• The model uses landmark coordinates, not raw images, for better efficiency.
• For best results: use a clean background and keep your hand visible and centered.
• Supports static ASL letters (A–Z), excluding motion-based gestures like "J" and "Z".

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🤝 Contributing

Pull requests are welcome! Feel free to fork or open an issue.

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📄 License

This project is open-source under the MIT License.

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👨‍💻 Author

Vivan Rajath
GitHub: https://github.com/VivanRajath