SIGNLINK : Sign Language Detection and Translation Application

This Flask application utilizes computer vision techniques to detect hand signs and provide real-time translation. The application leverages MediaPipe for hand landmark detection and a custom keypoint classifier to recognize hand signs. It also features two modes: one for learning gestures and another for translating them into text.

Features

• Hand Sign Detection: Uses a webcam to capture hand movements and recognize specific hand signs.
• Gesture Learning: Allows users to learn different gestures.
• Sign Translation: Translates recognized signs into text.
• Real-time Video Streaming: Displays the camera feed with detected hand signs and relevant information.

Requirements

• Python 3.x
• Flask
• OpenCV
• MediaPipe
• Other dependencies specified in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/hammadali1805/SignLink.git
   cd SignLink

2. Install the required packages:

   pip install -r requirements.txt

3. Make sure to update any paths in the code if needed.

Usage

1. Start the Flask application:

   python my_app.py

2. Open your web browser and navigate to http://127.0.0.1:5000/.

3. Use the following endpoints:

   • / - Home page
   • /learn - Learning mode for hand signs
   • /translate - Translation mode for hand signs
   • /sign - Get the currently selected sign
   • /detectedsign - Get the detected sign
   • /video/<usecase> - Real-time video streaming (use learn or translate as the use case)

Controls

• Use keys 0-9 to select different hand signs.
• Press n to switch to learning mode.
• Press k to switch to translation mode.
• Press h for help or information about current modes.
• Press ESC to exit the application.

Acknowledgments

• MediaPipe: https://google.github.io/mediapipe/
• OpenCV: https://opencv.org/
• Other resources and libraries utilized in the project.

License

This project is licensed under the MIT License - see the LICENSE file for details.