Real-time sign language detection: Empowering the disabled community

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-08-08 DOI:10.1016/j.mex.2024.102901
{"title":"Real-time sign language detection: Empowering the disabled community","authors":"","doi":"10.1016/j.mex.2024.102901","DOIUrl":null,"url":null,"abstract":"<div><p>Interaction and communication for normal human beings are easier than for a person with disabilities like speaking and hearing who may face communication problems with other people. Sign Language helps reduce this communication gap between a normal and disabled person. The prior solutions proposed using several deep learning techniques, such as Convolutional Neural Networks, Support Vector Machines, and K-Nearest Neighbors, have either demonstrated low accuracy or have not been implemented as real-time working systems. This system addresses both issues effectively. This work extends the difficulties faced while classifying the characters in Indian Sign Language(ISL). It can identify a total of 23 hand poses of the ISL. The system uses a pre-trained VGG16 Convolution Neural Network(CNN) with an attention mechanism. The model's training uses the Adam optimizer and cross-entropy loss function. The results demonstrate the effectiveness of transfer learning for ISL classification, achieving an accuracy of 97.5 % with VGG16 and 99.8 % with VGG16 plus attention mechanism.</p><ul><li><span>•</span><span><p>Enabling quick and accurate sign language recognition with the help of trained model VGG16 with an attention mechanism.</p></span></li><li><span>•</span><span><p>The system does not require any external gloves or sensors, which helps to eliminate the need for physical sensors while simplifying the process with reduced costs.</p></span></li><li><span>•</span><span><p>Real-time processing makes the system more helpful for people with speaking and hearing disabilities, making it easier for them to communicate with other humans.</p></span></li></ul></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2215016124003534/pdfft?md5=f4ed0ed2c2e051e0fcb53b858a504e61&pid=1-s2.0-S2215016124003534-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MethodsX","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215016124003534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Interaction and communication for normal human beings are easier than for a person with disabilities like speaking and hearing who may face communication problems with other people. Sign Language helps reduce this communication gap between a normal and disabled person. The prior solutions proposed using several deep learning techniques, such as Convolutional Neural Networks, Support Vector Machines, and K-Nearest Neighbors, have either demonstrated low accuracy or have not been implemented as real-time working systems. This system addresses both issues effectively. This work extends the difficulties faced while classifying the characters in Indian Sign Language(ISL). It can identify a total of 23 hand poses of the ISL. The system uses a pre-trained VGG16 Convolution Neural Network(CNN) with an attention mechanism. The model's training uses the Adam optimizer and cross-entropy loss function. The results demonstrate the effectiveness of transfer learning for ISL classification, achieving an accuracy of 97.5 % with VGG16 and 99.8 % with VGG16 plus attention mechanism.

  • Enabling quick and accurate sign language recognition with the help of trained model VGG16 with an attention mechanism.

  • The system does not require any external gloves or sensors, which helps to eliminate the need for physical sensors while simplifying the process with reduced costs.

  • Real-time processing makes the system more helpful for people with speaking and hearing disabilities, making it easier for them to communicate with other humans.

Abstract Image

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
实时手语检测:增强残疾人群体的能力
正常人的互动和交流要比有语言和听力障碍的人容易得多,因为后者可能会面临与他人交流的问题。手语有助于缩小正常人与残疾人之间的交流差距。之前提出的使用卷积神经网络、支持向量机和 K-最近邻等深度学习技术的解决方案要么准确率低,要么没有作为实时工作系统实施。本系统有效地解决了这两个问题。这项工作扩展了印度手语(ISL)字符分类所面临的困难。它可以识别印度手语中总共 23 种手部姿势。该系统使用了一个带有注意力机制的预训练 VGG16 卷积神经网络(CNN)。该模型的训练使用了 Adam 优化器和交叉熵损失函数。结果证明了迁移学习在 ISL 分类中的有效性,使用 VGG16 的准确率达到 97.5%,使用 VGG16 加注意力机制的准确率达到 99.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
期刊最新文献
ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization Standardized lab-scale production of the recombinant fusion protein HUG for the nanoscale analysis of bilirubin The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS A method to improve binary forecast skill verification Automated prediction of phosphorus concentration in soils using reflectance spectroscopy and machine learning algorithms
×
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