This project is an American Sign Language (ASL) detection system that recognizes hand gestures using computer vision and machine learning and converts them into speech.
The system uses Mediapipe for hand landmark detection, a pre-trained model for gesture classification, and Text-to-Speech (TTS) for converting recognized text into speech.
The graphical user interface (GUI) is built using Tkinter.
- Real-time ASL detection using webcam
- Hand landmark detection with Mediapipe
- Machine learning-based gesture classification
- Automatic word and sentence formation from detected gestures
- Text-to-Speech (TTS) conversion for speaking out sentences
- Graphical User Interface (GUI) for interaction
- Pause, Reset, and Speak Sentence options
- Video Capture: The system captures video frames from the webcam.
- Hand Detection: Mediapipe detects hand landmarks in the video frame.
- Feature Extraction: The system extracts hand landmark coordinates.
- Gesture Classification: A trained ML model predicts the ASL gesture.
- Text Formation: Recognized gestures are formed into words and sentences.
- Speech Conversion: The final sentence is converted into speech using TTS.
- GUI Interaction: Users can view recognized gestures, sentences, and control the system via buttons.
Ensure you have Python installed (version 3.x recommended). Install dependencies using:
pip install -r requirements.txt- OpenCV (cv2)
- Mediapipe
- Numpy
- Tkinter (built-in in Python)
- Pyttsx3 (for Text-to-Speech)
- Pickle (for model loading)
To start the ASL detection system, run:
python Main.py