{"title":"用递归神经网络识别匈牙利语的拼写字母","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":"{\"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}","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}
Recognition of the Hungarian fingerspelling alphabet using Recurrent Neural Network
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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