Jose Emiliano Lopez-Noriega, Miguel Ivan Fernandez-Valladares, Víctor Uc Cetina
{"title":"Glove-based sign language recognition solution to assist communication for deaf users","authors":"Jose Emiliano Lopez-Noriega, Miguel Ivan Fernandez-Valladares, Víctor Uc Cetina","doi":"10.1109/ICEEE.2014.6978268","DOIUrl":null,"url":null,"abstract":"This manuscript presents the research and development of a software that help deaf-mute communication by identifying the position of the fingers of the hand with 5DT gloves. The sign language is adopted by nearly all people with hearing deficiency, making it their main form of communication, but this communication is only successfully achieved if all the participants of the conversation are familiar with the sign language. The goal is to be able to translate hand signs into words and phrases with the possibility to send audio signals to allow deaf-mute users to communicate to people not familiar with the sign language. The recognition of hand gestures is accomplished using a neural network tested using five different training algorithms. A cross-validation experiment is provided to illustrate the robustness of our methods.","PeriodicalId":6661,"journal":{"name":"2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","volume":"81 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2014.6978268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
This manuscript presents the research and development of a software that help deaf-mute communication by identifying the position of the fingers of the hand with 5DT gloves. The sign language is adopted by nearly all people with hearing deficiency, making it their main form of communication, but this communication is only successfully achieved if all the participants of the conversation are familiar with the sign language. The goal is to be able to translate hand signs into words and phrases with the possibility to send audio signals to allow deaf-mute users to communicate to people not familiar with the sign language. The recognition of hand gestures is accomplished using a neural network tested using five different training algorithms. A cross-validation experiment is provided to illustrate the robustness of our methods.