{"title":"基于支持向量机的纸币号码识别新算法","authors":"S. Gai, Guowei Yang, S. Zhang, M. Wan","doi":"10.1109/ACPR.2013.115","DOIUrl":null,"url":null,"abstract":"Detecting the banknote serial number is an important task in business transaction. In this paper, we propose a new banknote number recognition method. The preprocessing of each banknote image is used to locate position of the banknote number image. Each number image is divided into non-overlapping partitions and the average gray value of each partition is used as feature vector for recognition. The optimal kernel function is obtained by the semi-definite programming (SDP). The experimental results show that the proposed method outperforms MASK, BP, HMM, Single SVM classifiers.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"170 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"New Banknote Number Recognition Algorithm Based on Support Vector Machine\",\"authors\":\"S. Gai, Guowei Yang, S. Zhang, M. Wan\",\"doi\":\"10.1109/ACPR.2013.115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detecting the banknote serial number is an important task in business transaction. In this paper, we propose a new banknote number recognition method. The preprocessing of each banknote image is used to locate position of the banknote number image. Each number image is divided into non-overlapping partitions and the average gray value of each partition is used as feature vector for recognition. The optimal kernel function is obtained by the semi-definite programming (SDP). The experimental results show that the proposed method outperforms MASK, BP, HMM, Single SVM classifiers.\",\"PeriodicalId\":365633,\"journal\":{\"name\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"volume\":\"170 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACPR.2013.115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 2nd IAPR Asian Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2013.115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New Banknote Number Recognition Algorithm Based on Support Vector Machine
Detecting the banknote serial number is an important task in business transaction. In this paper, we propose a new banknote number recognition method. The preprocessing of each banknote image is used to locate position of the banknote number image. Each number image is divided into non-overlapping partitions and the average gray value of each partition is used as feature vector for recognition. The optimal kernel function is obtained by the semi-definite programming (SDP). The experimental results show that the proposed method outperforms MASK, BP, HMM, Single SVM classifiers.