Naoldaba/Amharic-SignLanguageRecognition

# Amharic Sign Language Recognition This project is an Amharic sign language recognition system that uses a webcam to detect hand gestures and predict corresponding Amharic letters in real-time. The project leverages **Mediapipe** for hand landmark detection, **OpenCV** for capturing video frames and drawing bounding boxes, and a **Random Forest Classifier** trained on hand landmarks for letter classification. ## Features - Real-time hand gesture recognition using a webcam. - Prediction of Amharic letters based on hand landmarks. - Custom Amharic font rendering using the **PIL** library to display predictions on the screen. ## Prerequisites Before you begin, ensure you have the following installed on your system: - Python 3.x - Mediapipe (`mediapipe` library) - OpenCV (`opencv-python`) - PIL (Pillow) - NumPy - Scikit-learn - A trained classifier model stored in a `.p` file (Pickle format) You can install the required Python packages with: ```bash pip install mediapipe opencv-python pillow numpy scikit-learn ``` ## installation - git clone github.com - cd Amharic-SignLanguageRecognition ## Running the Model - python sign_language_predictor.py