Youssef Farhan, Abdessalam Ait Madi, Abdennour Ryahi, Fatima Derwich
{"title":"美国手语:检测和自动文本生成","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":"{\"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}","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}
American Sign Language: Detection and Automatic Text Generation
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.