Youssef Farhan, Abdessalam Ait Madi, Abdennour Ryahi, Fatima Derwich
{"title":"American Sign Language: Detection and Automatic Text Generation","authors":"Youssef Farhan, Abdessalam Ait Madi, Abdennour Ryahi, Fatima Derwich","doi":"10.1109/IRASET52964.2022.9738061","DOIUrl":null,"url":null,"abstract":"In our daily life, communication is crucial. For people who are hard of hearing (deaf or/and mute) sign language is the means of their communication. Nonetheless, many people are still unaware of sign languages, resulting in a communication gap. To improve communication between deaf-mutes and the hearing majority, this paper proposes an ASL (American Sign Language) detection system for 26 alphabets and three assisted signs, which can detect ASL captured by a standard computer camera, then generate an automatic text. SSD-MobileNet is used as the object detection model. The proposed system achieved precision and recall of 82.8% and 85.5% respectively.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRASET52964.2022.9738061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In our daily life, communication is crucial. For people who are hard of hearing (deaf or/and mute) sign language is the means of their communication. Nonetheless, many people are still unaware of sign languages, resulting in a communication gap. To improve communication between deaf-mutes and the hearing majority, this paper proposes an ASL (American Sign Language) detection system for 26 alphabets and three assisted signs, which can detect ASL captured by a standard computer camera, then generate an automatic text. SSD-MobileNet is used as the object detection model. The proposed system achieved precision and recall of 82.8% and 85.5% respectively.