{"title":"Recognition and Translation of Hand Gestures to Urdu Alphabets Using a Geometrical Classification","authors":"Huma Tauseef, M. A. Fahiem, Saima Farhan","doi":"10.1109/VIZ.2009.11","DOIUrl":null,"url":null,"abstract":"Recognition of hand gestures associated with different alphabets is a very important research area. The persons with vocal and hearing disabilities may benefit a lot from this research. The communication gap between these and normal persons can be filled by providing an aid in the form of a computerized translation of different hand gestures. In this paper, we have developed a recognition system to translate hand gestures into Urdu alphabets. We have formulated a comprehensive classification scheme which is core to this recognition system. The accuracy rate of our approach is 97.4% which is better than the previous approaches.","PeriodicalId":315752,"journal":{"name":"2009 Second International Conference in Visualisation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Second International Conference in Visualisation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VIZ.2009.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
Recognition of hand gestures associated with different alphabets is a very important research area. The persons with vocal and hearing disabilities may benefit a lot from this research. The communication gap between these and normal persons can be filled by providing an aid in the form of a computerized translation of different hand gestures. In this paper, we have developed a recognition system to translate hand gestures into Urdu alphabets. We have formulated a comprehensive classification scheme which is core to this recognition system. The accuracy rate of our approach is 97.4% which is better than the previous approaches.
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