{"title":"手写识别的人工神经网络方法","authors":"W. Goh, D. Mital, H. Babri","doi":"10.1109/KES.1997.616872","DOIUrl":null,"url":null,"abstract":"This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second.","PeriodicalId":166931,"journal":{"name":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"An artificial neural network approach to handwriting recognition\",\"authors\":\"W. Goh, D. Mital, H. Babri\",\"doi\":\"10.1109/KES.1997.616872\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second.\",\"PeriodicalId\":166931,\"journal\":{\"name\":\"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KES.1997.616872\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1997.616872","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An artificial neural network approach to handwriting recognition
This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second.