Takashi Kobayashi, Kaori Nakamura, Hirokazu Muramatsu, Takahiro Sugiyama, K. Abe
{"title":"Handwritten numeral recognition using flexible matching based on learning of stroke statistics","authors":"Takashi Kobayashi, Kaori Nakamura, Hirokazu Muramatsu, Takahiro Sugiyama, K. Abe","doi":"10.1109/ICDAR.2001.953862","DOIUrl":null,"url":null,"abstract":"The purpose of this study is to learn shapes and structures of a given learning set of handwritten numerals and to develop a flexible matching method for recognition based on the learning. First, this paper proposes a method of how to obtain a set of standard character patterns and the ranges of variations varying statistically from the given learning character samples. Then the recognition is made as follows: each standard pattern is deformed to match with the input character; and the matching is evaluated by the energy of deformation; and the closeness of the standard pattern to the input.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Sixth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2001.953862","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The purpose of this study is to learn shapes and structures of a given learning set of handwritten numerals and to develop a flexible matching method for recognition based on the learning. First, this paper proposes a method of how to obtain a set of standard character patterns and the ranges of variations varying statistically from the given learning character samples. Then the recognition is made as follows: each standard pattern is deformed to match with the input character; and the matching is evaluated by the energy of deformation; and the closeness of the standard pattern to the input.
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