{"title":"Recognition of the Hungarian fingerspelling alphabet using Recurrent Neural Network","authors":"Bence Dankó, Gábor Kertész","doi":"10.1109/SAMI.2019.8782725","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to introduce a Recurrent Convolutional Neural Network based on depth data to recognize the signs of the Hungarian fingerspelling alphabet. The training dataset contains depth data of 27 static and 15 dynamic signs. A 88.6% classification accuracy was measured for during the test with the recommended model in this paper, which is a special type of recurrent network containing LSTM and convolutional layers.","PeriodicalId":240256,"journal":{"name":"2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAMI.2019.8782725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The aim of this paper is to introduce a Recurrent Convolutional Neural Network based on depth data to recognize the signs of the Hungarian fingerspelling alphabet. The training dataset contains depth data of 27 static and 15 dynamic signs. A 88.6% classification accuracy was measured for during the test with the recommended model in this paper, which is a special type of recurrent network containing LSTM and convolutional layers.
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