Problem Statement

This project aims to translate American Sign Language (ASL) gestures into English text in real-time. By detecting and interpreting hand gestures, this application bridges the communication gap for individuals who use ASL.

Technologies Used

• YOLOv8: Used for real-time object detection and classification of hand gestures.
• cv2: OpenCV library for capturing and processing video feed from the webcam.
• Streamlit: Web application framework for creating the user interface.

File Structure

├── app.py                   # Streamlit application
├── best.pt                  # Trained model
├── main.py                  # Main application logic
├── requirements.txt         # Dependencies
├── .gitignore               # Git ignore file
└── README.md                # Project documentation

Working

1. Video Capture: The cv2 library captures the video feed from the webcam.
2. Gesture Detection: The captured video frames are fed into the YOLOv8 model, which detects and identifies the hand gestures.
3. Translation: The identified gestures are then mapped to their corresponding ASL signs.
4. Display: The translated English text is displayed in a Streamlit web application, providing an interactive and user-friendly interface.

Output:

Future Scope:

Currently, the model can identify nine hand signs: 'Hello', 'I Love You', 'One Second', 'Peace', 'Thumbs down', 'Thumbs up', 'Time Out', 'Hope' and 'Ok'. In future, more signs can be added for making the model a more powerful tool for communication, accessible to a broader range of users and applications.