ACCESSING INFORMATION FOR PHYSICALLY IMPAIRED PERSONS USING SIGN LANGUAGE DETECTION SYSTEM

Abstract

This paper presents a novel approach to improving information accessibility for physically impaired individuals, specifically those with hearing impairments, through the development and implementation of a sign language detection system. The system leverages state-of-the-art machine learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), combined with advanced computer vision techniques to accurately recognize and interpret sign language gestures in real-time. This technology is designed to bridge the communication gap by converting recognized gestures into text or spoken language, thereby facilitating access to a wide range of digital information and communication platforms.