Real time sign language detection

A real time sign language detector is a significant step forward in improving communication between the deaf and the general population. Real-time sign language detection is a critical tool for enhancing communication between individuals with hearing impairments and others. This research investigates the use of YOLOv3 (You Only Look Once version 3), a highly efficient object detection model, for real-time sign language recognition. YOLOv3 is known for its speed and accuracy, making it an ideal candidate for processing video frames in real-time to detect sign language gestures. The proposed system uses YOLOv3 to identify and classify hand gestures captured in video streams. A custom dataset of hand gesture images associated with specific sign language words or phrases is used to train the YOLOv3 model. The system performs real-time detection by applying the trained model to video frames, providing translations of sign language gestures. The implementation of YOLOv3 enables a fast and scalable solution for recognizing different sign language gestures, while maintaining a low processing delay suitable for real-time applications. The system's performance is evaluated based on accuracy, speed, and the ability to handle dynamic gestures. Results demonstrate the potential of YOLOv3 for efficient, real-time sign language detection, contributing to better communication in diverse settings, particularly for accessibility applications.