{"title":"联机变形德文汉字识别的联想记忆模型","authors":"Gaurav Pagare, K. Verma","doi":"10.1109/ICACC.2015.42","DOIUrl":null,"url":null,"abstract":"Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Associative Memory Model for Distorted On-Line Devanagari Character Recognition\",\"authors\":\"Gaurav Pagare, K. Verma\",\"doi\":\"10.1109/ICACC.2015.42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.\",\"PeriodicalId\":368544,\"journal\":{\"name\":\"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACC.2015.42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACC.2015.42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Associative Memory Model for Distorted On-Line Devanagari Character Recognition
Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.