利用手语检测系统为身体受损者获取信息

.Karthikeyan V.K
{"title":"利用手语检测系统为身体受损者获取信息","authors":".Karthikeyan V.K","doi":"10.55041/ijsrem34415","DOIUrl":null,"url":null,"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.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ACCESSING INFORMATION FOR PHYSICALLY IMPAIRED PERSONS USING SIGN LANGUAGE DETECTION SYSTEM\",\"authors\":\".Karthikeyan V.K\",\"doi\":\"10.55041/ijsrem34415\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":13661,\"journal\":{\"name\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55041/ijsrem34415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55041/ijsrem34415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ACCESSING INFORMATION FOR PHYSICALLY IMPAIRED PERSONS USING SIGN LANGUAGE DETECTION SYSTEM
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Exploring Vulnerabilities and Threats in Large Language Models: Safeguarding Against Exploitation and Misuse Experimental Investigation of Leachate Treatment Using Low-Cost Adsorbents Exploring Vulnerabilities and Threats in Large Language Models: Safeguarding Against Exploitation and Misuse BANK TRANSACTION USING IRIS AND BIOMETRIC Experimental Investigation of Leachate Treatment Using Low-Cost Adsorbents
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1