{"title":"在线波斯语字符的特征提取","authors":"V. Ghods, E. Kabir","doi":"10.1109/ICFHR.2010.81","DOIUrl":null,"url":null,"abstract":"This paper demonstrates the effectiveness of proper and efficient features for classifying online Farsi characters. We use these features to classify the main body of Farsi letters to nine groups. We implemented our method on the main bodies of 4000 isolated letters from \"TMU dataset\". Correct recognition rates of 99% and 94% were achieved for training and test sets respectively.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Feature Extraction for Online Farsi Characters\",\"authors\":\"V. Ghods, E. Kabir\",\"doi\":\"10.1109/ICFHR.2010.81\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper demonstrates the effectiveness of proper and efficient features for classifying online Farsi characters. We use these features to classify the main body of Farsi letters to nine groups. We implemented our method on the main bodies of 4000 isolated letters from \\\"TMU dataset\\\". Correct recognition rates of 99% and 94% were achieved for training and test sets respectively.\",\"PeriodicalId\":335044,\"journal\":{\"name\":\"2010 12th International Conference on Frontiers in Handwriting Recognition\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 12th International Conference on Frontiers in Handwriting Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFHR.2010.81\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 12th International Conference on Frontiers in Handwriting Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFHR.2010.81","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper demonstrates the effectiveness of proper and efficient features for classifying online Farsi characters. We use these features to classify the main body of Farsi letters to nine groups. We implemented our method on the main bodies of 4000 isolated letters from "TMU dataset". Correct recognition rates of 99% and 94% were achieved for training and test sets respectively.
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