{"title":"Amharic Character Image Recognition","authors":"B. Belay, T. Habtegebrial, D. Stricker","doi":"10.1109/ICCT.2018.8599888","DOIUrl":null,"url":null,"abstract":"In this paper we introduce Convolutional Neural Network (CNN) based method for Amharic character image recognition. We also introduce a dataset for training purposes. The proposed method has less pre-processing steps and out per- forms the state-of-the-art by a large margin. Experiments were done on 80,000 Amharic character images which was generated with different degradation level. We systematically evaluated the performance of the recognition model and achieved the state-of- art performance with an average recognition accuracy of 92.71%.","PeriodicalId":244952,"journal":{"name":"2018 IEEE 18th International Conference on Communication Technology (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 18th International Conference on Communication Technology (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT.2018.8599888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
In this paper we introduce Convolutional Neural Network (CNN) based method for Amharic character image recognition. We also introduce a dataset for training purposes. The proposed method has less pre-processing steps and out per- forms the state-of-the-art by a large margin. Experiments were done on 80,000 Amharic character images which was generated with different degradation level. We systematically evaluated the performance of the recognition model and achieved the state-of- art performance with an average recognition accuracy of 92.71%.