{"title":"Matching of characters in scene images by using local shape feature vectors","authors":"Y. Hu, T. Nagao","doi":"10.1109/ICIAP.2003.1234051","DOIUrl":null,"url":null,"abstract":"The paper describes a new method for locating and recognizing colored patterns of characters in complex scene images where translation, rotation, scale and contrast are unknown. A model of local shape feature vectors is presented. It consists of three vectors and represents some identifiable features in a pattern of characters. Based on this model, potential search points are first found from an unknown target image with this model matched to its edge image. Then, a template matching technique is employed on these candidate points, and the results are classified by a simple nearest neighborhood method and a best match is finally picked out in each cluster. Thus, multiple instances of a pattern of characters are matched and recognized. Experimental results demonstrate the effectiveness of this method.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAP.2003.1234051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The paper describes a new method for locating and recognizing colored patterns of characters in complex scene images where translation, rotation, scale and contrast are unknown. A model of local shape feature vectors is presented. It consists of three vectors and represents some identifiable features in a pattern of characters. Based on this model, potential search points are first found from an unknown target image with this model matched to its edge image. Then, a template matching technique is employed on these candidate points, and the results are classified by a simple nearest neighborhood method and a best match is finally picked out in each cluster. Thus, multiple instances of a pattern of characters are matched and recognized. Experimental results demonstrate the effectiveness of this method.