{"title":"基于改进概率神经网络(PNN)的ISBN识别","authors":"Chi Hau Chen, G. You","doi":"10.1109/ICPR.1992.201807","DOIUrl":null,"url":null,"abstract":"Develops a modified probabilistic neural network (PNN) to recognize the international standard book number (ISBN). Effort is made for real time implementation of the recognition process. The authors used a simple but effective feature extraction approach, which takes the meshed averages of an isolated character as feature vector. To achieve better performance they modified the original PNN algorithm. Before normalizing the feature vectors onto a unit hyper-sphere, they are mapped into a one more dimension higher hyper-cube than the feature vector. The best result for correct characters classification is 99.62%.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":"110 1","pages":"419-421"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"ISBN recognition using a modified probabilistic neural network (PNN)\",\"authors\":\"Chi Hau Chen, G. You\",\"doi\":\"10.1109/ICPR.1992.201807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Develops a modified probabilistic neural network (PNN) to recognize the international standard book number (ISBN). Effort is made for real time implementation of the recognition process. The authors used a simple but effective feature extraction approach, which takes the meshed averages of an isolated character as feature vector. To achieve better performance they modified the original PNN algorithm. Before normalizing the feature vectors onto a unit hyper-sphere, they are mapped into a one more dimension higher hyper-cube than the feature vector. The best result for correct characters classification is 99.62%.<<ETX>>\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":\"110 1\",\"pages\":\"419-421\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"模式识别与人工智能\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1992.201807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
ISBN recognition using a modified probabilistic neural network (PNN)
Develops a modified probabilistic neural network (PNN) to recognize the international standard book number (ISBN). Effort is made for real time implementation of the recognition process. The authors used a simple but effective feature extraction approach, which takes the meshed averages of an isolated character as feature vector. To achieve better performance they modified the original PNN algorithm. Before normalizing the feature vectors onto a unit hyper-sphere, they are mapped into a one more dimension higher hyper-cube than the feature vector. The best result for correct characters classification is 99.62%.<>