ACCESSING INFORMATION FOR PHYSICALLY IMPAIRED PERSONS USING SIGN LANGUAGE DETECTION SYSTEM

.Karthikeyan V.K
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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.
利用手语检测系统为身体受损者获取信息
本文介绍了一种新颖的方法,通过开发和实施手语检测系统,提高身体受损者(特别是听力受损者)的信息无障碍程度。该系统利用最先进的机器学习算法,包括卷积神经网络(CNN)和递归神经网络(RNN),结合先进的计算机视觉技术,实时准确地识别和解释手语手势。该技术旨在将识别到的手势转换为文本或口语,从而为访问广泛的数字信息和通信平台提供便利,为沟通架起桥梁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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