Overview
Hand sign detection is a computer vision project that aims to detect and recognize hand signs from images or live video feeds. The goal of this project is to build a real-time hand sign recognition system capable of identifying common hand signs, such as letters from the American Sign Language (ASL) or specific gestures.
Features
Hand Detection: The system utilizes a hand detection model to identify the presence and location of hands within the input images or video frames.
Hand Sign Recognition: Once the hands are detected, the system uses a hand sign recognition model to identify and classify the hand signs being displayed.
Real-time Processing: The project is optimized for real-time processing, allowing users to use their webcams or video streams for instant hand sign recognition.
Support for Multiple Gestures: The system supports the recognition of multiple hand signs or gestures, providing a diverse set of sign language letters or custom gestures.
User-friendly Interface: The project includes a user-friendly interface that displays the input feed, detected hands, and the recognized hand sign with intuitive visualization.
Prerequisites
Python (version 3.6 or higher) OpenCV library TensorFlow or PyTorch (depending on the chosen hand detection and recognition models) Webcam or video input device (for real-time processing)