/Hand-Gesture

real-time hand sign detection and recognition using computer vision techniques. The system accurately identifies left or right hand signs and dynamically updates the displayed signs.

Primary LanguageJupyter Notebook

Real-Time Hand Sign Detection and Recognition

Overview

This project focuses on real-time hand sign detection and recognition using computer vision techniques. The system accurately identifies left or right hand signs and dynamically updates the displayed signs.

Features

  • Real-Time Detection: Utilizes computer vision with MediaPipe for precise hand tracking and dynamic sign recognition.
  • Python Libraries: Implements OpenCV for image processing, MediaPipe for hand tracking, and a Convolutional Neural Network (CNN) for sign recognition.
  • Dynamic Visualization: Provides live updates of detected hand signs with a user-friendly interface.

Technologies Used

  • Python Libraries: OpenCV, MediaPipe, TensorFlow
  • Web Framework: Flask (for optional web-based deployment)

Installation

  1. Clone the repository:

    git clone https://github.com/theSuriya/Hand-Gesture.git

Sample Video

fbe0afb7-2a0b-4739-af1f-74f38f46d6b2.mp4