{"title":"近似识别文本搜索的贝叶斯相似度模型估计","authors":"A. Takasu","doi":"10.1109/ICDAR.2009.193","DOIUrl":null,"url":null,"abstract":"Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Bayesian Similarity Model Estimation for Approximate Recognized Text Search\",\"authors\":\"A. Takasu\",\"doi\":\"10.1109/ICDAR.2009.193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.\",\"PeriodicalId\":433762,\"journal\":{\"name\":\"2009 10th International Conference on Document Analysis and Recognition\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 10th International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2009.193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 10th International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2009.193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian Similarity Model Estimation for Approximate Recognized Text Search
Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.