{"title":"脱机手写词识别在印地语","authors":"R. Sitaram, Shrang Jain, Hariharan Ravishankar","doi":"10.1145/2432553.2432563","DOIUrl":null,"url":null,"abstract":"This paper discusses the Hindi offline handwritten word recognizer (HWR) that we are developing. For the purpose of training and testing the offline HWR, we have created a Hindi handwritten word and character database from 100 writers. In our HWR we use two-pass Dynamic Programming algorithm to match the test word against each word in the lexicon by initially segmenting the test word image into probable characters. We extract directional element features (DEF) on each character image segment and statistically model them. Currently we are achieving word recognition accuracies of 91.23% to 79.94% on 10 to 30 vocabulary words.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Offline handwritten word recognition in Hindi\",\"authors\":\"R. Sitaram, Shrang Jain, Hariharan Ravishankar\",\"doi\":\"10.1145/2432553.2432563\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses the Hindi offline handwritten word recognizer (HWR) that we are developing. For the purpose of training and testing the offline HWR, we have created a Hindi handwritten word and character database from 100 writers. In our HWR we use two-pass Dynamic Programming algorithm to match the test word against each word in the lexicon by initially segmenting the test word image into probable characters. We extract directional element features (DEF) on each character image segment and statistically model them. Currently we are achieving word recognition accuracies of 91.23% to 79.94% on 10 to 30 vocabulary words.\",\"PeriodicalId\":410986,\"journal\":{\"name\":\"DAR '12\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DAR '12\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2432553.2432563\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DAR '12","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2432553.2432563","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper discusses the Hindi offline handwritten word recognizer (HWR) that we are developing. For the purpose of training and testing the offline HWR, we have created a Hindi handwritten word and character database from 100 writers. In our HWR we use two-pass Dynamic Programming algorithm to match the test word against each word in the lexicon by initially segmenting the test word image into probable characters. We extract directional element features (DEF) on each character image segment and statistically model them. Currently we are achieving word recognition accuracies of 91.23% to 79.94% on 10 to 30 vocabulary words.
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