{"title":"手写高棉文字识别","authors":"Bayram Annanurov, N. Noor","doi":"10.1109/WIECON-ECE.2016.8009112","DOIUrl":null,"url":null,"abstract":"This paper proposes a model for an offline handwritten Khmer character recognition. We make use of two dimensional Fourier transformation for feature selection and feed-forward Artificial Neural Net as classification tool. The recognition system allows using the nature of Khmer writing, which is an example of alphasyllabary (Abugida) writing systems. The recognition of the normalized handwritten images has been performed for comparison purposes. The results suggest that the recognition rate increases with reduced feature set.","PeriodicalId":412645,"journal":{"name":"2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Handwritten Khmer text recognition\",\"authors\":\"Bayram Annanurov, N. Noor\",\"doi\":\"10.1109/WIECON-ECE.2016.8009112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a model for an offline handwritten Khmer character recognition. We make use of two dimensional Fourier transformation for feature selection and feed-forward Artificial Neural Net as classification tool. The recognition system allows using the nature of Khmer writing, which is an example of alphasyllabary (Abugida) writing systems. The recognition of the normalized handwritten images has been performed for comparison purposes. The results suggest that the recognition rate increases with reduced feature set.\",\"PeriodicalId\":412645,\"journal\":{\"name\":\"2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WIECON-ECE.2016.8009112\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIECON-ECE.2016.8009112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a model for an offline handwritten Khmer character recognition. We make use of two dimensional Fourier transformation for feature selection and feed-forward Artificial Neural Net as classification tool. The recognition system allows using the nature of Khmer writing, which is an example of alphasyllabary (Abugida) writing systems. The recognition of the normalized handwritten images has been performed for comparison purposes. The results suggest that the recognition rate increases with reduced feature set.
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