{"title":"基于分层多层感知器的场景文本检测","authors":"Gang Zhou, Yuehu Liu, Jianji Wang","doi":"10.1109/ICINFA.2011.5948990","DOIUrl":null,"url":null,"abstract":"In this paper, a new scene text detection method based on hierarchical multilayer perceptron (MLP) is proposed. First, connected components (CCs) are segmented locally by text probability map. Then, a novelty hierarchical architecture consisting of two MLP classifiers in tandem is utilized to analysis the CCs. In this hierarchical setup, the first stage MLP classifier is trained using unary property features. The second stage MLP classifier is trained for CCs pairs including both posterior probabilities estimated by first stage and relationship features. Finally, candidate text CCs are grouping into words. Experimental results evaluated on the public dataset show that our approach yields better performance compared with state-of-the-art methods.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Scene text detection based on hierarchical multilayer perceptron\",\"authors\":\"Gang Zhou, Yuehu Liu, Jianji Wang\",\"doi\":\"10.1109/ICINFA.2011.5948990\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new scene text detection method based on hierarchical multilayer perceptron (MLP) is proposed. First, connected components (CCs) are segmented locally by text probability map. Then, a novelty hierarchical architecture consisting of two MLP classifiers in tandem is utilized to analysis the CCs. In this hierarchical setup, the first stage MLP classifier is trained using unary property features. The second stage MLP classifier is trained for CCs pairs including both posterior probabilities estimated by first stage and relationship features. Finally, candidate text CCs are grouping into words. Experimental results evaluated on the public dataset show that our approach yields better performance compared with state-of-the-art methods.\",\"PeriodicalId\":299418,\"journal\":{\"name\":\"2011 IEEE International Conference on Information and Automation\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Information and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2011.5948990\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5948990","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scene text detection based on hierarchical multilayer perceptron
In this paper, a new scene text detection method based on hierarchical multilayer perceptron (MLP) is proposed. First, connected components (CCs) are segmented locally by text probability map. Then, a novelty hierarchical architecture consisting of two MLP classifiers in tandem is utilized to analysis the CCs. In this hierarchical setup, the first stage MLP classifier is trained using unary property features. The second stage MLP classifier is trained for CCs pairs including both posterior probabilities estimated by first stage and relationship features. Finally, candidate text CCs are grouping into words. Experimental results evaluated on the public dataset show that our approach yields better performance compared with state-of-the-art methods.