G. S. Reddy, Bandita Sarma, R. Naik, S. Prasanna, C. Mahanta
{"title":"使用隐马尔可夫模型的阿萨姆在线手写数字识别系统","authors":"G. S. Reddy, Bandita Sarma, R. Naik, S. Prasanna, C. Mahanta","doi":"10.1145/2432553.2432573","DOIUrl":null,"url":null,"abstract":"This work describes the development of Assamese online handwritten digit recognition system. Assamese numerals are the same as the Bangla numerals. A large database of handwritten numerals is collected and partitioned into two parts of equal size. The first part is used for developing the Hidden Markov Models (HMM) based digit models. The (x, y) coordinates and their first and second time derivatives are used as features. The second part of the database is tested against the models to evaluate the performance. The digit recognition system provides an average recognition performance of 96.02%. A large amount of confusion is observed among the numerals 5 & 6. The new distance feature is used as an additional feature and the models are retrained. The performance for numeral 5 & 6 increases from 91.60% & 95.40% to 95.30% & 94.90%. As a result, the confusion reduces significantly and the average recognition performance increases to 97.14%.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Assamese online handwritten digit recognition system using hidden Markov models\",\"authors\":\"G. S. Reddy, Bandita Sarma, R. Naik, S. Prasanna, C. Mahanta\",\"doi\":\"10.1145/2432553.2432573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work describes the development of Assamese online handwritten digit recognition system. Assamese numerals are the same as the Bangla numerals. A large database of handwritten numerals is collected and partitioned into two parts of equal size. The first part is used for developing the Hidden Markov Models (HMM) based digit models. The (x, y) coordinates and their first and second time derivatives are used as features. The second part of the database is tested against the models to evaluate the performance. The digit recognition system provides an average recognition performance of 96.02%. A large amount of confusion is observed among the numerals 5 & 6. The new distance feature is used as an additional feature and the models are retrained. The performance for numeral 5 & 6 increases from 91.60% & 95.40% to 95.30% & 94.90%. As a result, the confusion reduces significantly and the average recognition performance increases to 97.14%.\",\"PeriodicalId\":410986,\"journal\":{\"name\":\"DAR '12\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DAR '12\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2432553.2432573\",\"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/10.1145/2432553.2432573","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assamese online handwritten digit recognition system using hidden Markov models
This work describes the development of Assamese online handwritten digit recognition system. Assamese numerals are the same as the Bangla numerals. A large database of handwritten numerals is collected and partitioned into two parts of equal size. The first part is used for developing the Hidden Markov Models (HMM) based digit models. The (x, y) coordinates and their first and second time derivatives are used as features. The second part of the database is tested against the models to evaluate the performance. The digit recognition system provides an average recognition performance of 96.02%. A large amount of confusion is observed among the numerals 5 & 6. The new distance feature is used as an additional feature and the models are retrained. The performance for numeral 5 & 6 increases from 91.60% & 95.40% to 95.30% & 94.90%. As a result, the confusion reduces significantly and the average recognition performance increases to 97.14%.