{"title":"Greek sign language vocabulary recognition using Kinect","authors":"Nikolaos Gkigkelos, C. Goumopoulos","doi":"10.1145/3139367.3139386","DOIUrl":null,"url":null,"abstract":"Sign language recognition is a challenging problem both when tracking continuous signs (communication mode) or single words (translation mode)1. We have developed a system that can recognize Greek sign language vocabulary in translation mode using Kinect technology. The sensor captures 3D hands movement trajectory and then a set of features in the form of body joints are fed to a classifier to recognize the input sign. Normalization is used to align test and stored trajectories using the dynamic time warping algorithm before matching is done using the Nearest-Neighbor approach. The low computational complexity of the involved algorithms allows for building a system with real-time response times. The system was evaluated with a sample of 5 individuals and is capable of recognizing 15 signs of the Greek sign language. Different configurations were tested and the best accuracy achieved was 99.33%.","PeriodicalId":436862,"journal":{"name":"Proceedings of the 21st Pan-Hellenic Conference on Informatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st Pan-Hellenic Conference on Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3139367.3139386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Sign language recognition is a challenging problem both when tracking continuous signs (communication mode) or single words (translation mode)1. We have developed a system that can recognize Greek sign language vocabulary in translation mode using Kinect technology. The sensor captures 3D hands movement trajectory and then a set of features in the form of body joints are fed to a classifier to recognize the input sign. Normalization is used to align test and stored trajectories using the dynamic time warping algorithm before matching is done using the Nearest-Neighbor approach. The low computational complexity of the involved algorithms allows for building a system with real-time response times. The system was evaluated with a sample of 5 individuals and is capable of recognizing 15 signs of the Greek sign language. Different configurations were tested and the best accuracy achieved was 99.33%.