使用深度学习的印度手语识别

Priyanka Mistry, Vedang Jotaniya, Parth Patel, Narendra Patel, Mosin Hasan
{"title":"使用深度学习的印度手语识别","authors":"Priyanka Mistry, Vedang Jotaniya, Parth Patel, Narendra Patel, Mosin Hasan","doi":"10.1109/aimv53313.2021.9670933","DOIUrl":null,"url":null,"abstract":"Sign language is used by people having speaking and hearing disabilities. It generally has a set of words, where each word is represented by one or more hand gestures in sequence and may contain facial expressions. In order to address the interpretation/translation from sign language to English Language, we present our sign recognition approach for Indian sign language which aims to provide a method for interpreting signs in Indian sign language to words in English language translation. The approach is to have a vision based system in which the sequence of images representing a word in ISL is translated to equivalent English word. The translation would be done by means of Deep learning algorithms namely convolutional neural nets and recurrent neural nets. The system will be analyzing sequence of images, hence CNNs will analyze each image and their sequence is analyzed by LSTM (which is an implementation of RNN). We divided dataset into training dataset and testing dataset, which obtained 73.60% accuracy. The image distributions are kept fairly different in training and testing datasets.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Indian Sign Language Recognition using Deep Learning\",\"authors\":\"Priyanka Mistry, Vedang Jotaniya, Parth Patel, Narendra Patel, Mosin Hasan\",\"doi\":\"10.1109/aimv53313.2021.9670933\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sign language is used by people having speaking and hearing disabilities. It generally has a set of words, where each word is represented by one or more hand gestures in sequence and may contain facial expressions. In order to address the interpretation/translation from sign language to English Language, we present our sign recognition approach for Indian sign language which aims to provide a method for interpreting signs in Indian sign language to words in English language translation. The approach is to have a vision based system in which the sequence of images representing a word in ISL is translated to equivalent English word. The translation would be done by means of Deep learning algorithms namely convolutional neural nets and recurrent neural nets. The system will be analyzing sequence of images, hence CNNs will analyze each image and their sequence is analyzed by LSTM (which is an implementation of RNN). We divided dataset into training dataset and testing dataset, which obtained 73.60% accuracy. The image distributions are kept fairly different in training and testing datasets.\",\"PeriodicalId\":135318,\"journal\":{\"name\":\"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/aimv53313.2021.9670933\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/aimv53313.2021.9670933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

手语是由有语言和听力障碍的人使用的。它通常有一组单词,其中每个单词由一个或多个顺序的手势表示,并且可能包含面部表情。为了解决从手语到英语的口译/翻译问题,我们提出了印度手语的手语识别方法,旨在提供一种将印度手语中的手势翻译成英语翻译中的单词的方法。该方法是建立一个基于视觉的系统,其中表示ISL中的单词的图像序列被翻译成等效的英语单词。翻译将通过深度学习算法,即卷积神经网络和循环神经网络来完成。系统将分析图像序列,因此cnn将分析每个图像,并通过LSTM (RNN的一种实现)分析它们的序列。我们将数据集分为训练数据集和测试数据集,准确率达到73.60%。在训练和测试数据集中,图像分布保持相当不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indian Sign Language Recognition using Deep Learning
Sign language is used by people having speaking and hearing disabilities. It generally has a set of words, where each word is represented by one or more hand gestures in sequence and may contain facial expressions. In order to address the interpretation/translation from sign language to English Language, we present our sign recognition approach for Indian sign language which aims to provide a method for interpreting signs in Indian sign language to words in English language translation. The approach is to have a vision based system in which the sequence of images representing a word in ISL is translated to equivalent English word. The translation would be done by means of Deep learning algorithms namely convolutional neural nets and recurrent neural nets. The system will be analyzing sequence of images, hence CNNs will analyze each image and their sequence is analyzed by LSTM (which is an implementation of RNN). We divided dataset into training dataset and testing dataset, which obtained 73.60% accuracy. The image distributions are kept fairly different in training and testing datasets.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信