{"title":"基于多神经网络的机器打印字符识别自动规则生成","authors":"J. Wang, J. Jean","doi":"10.1109/ICSYSE.1991.161148","DOIUrl":null,"url":null,"abstract":"A set of neural networks is used in a two-stage character recognition system to resolve the confusion among similar characters. A snowball training algorithm is proposed to remedy the convergence problem encountered by backpropagation training. The algorithm is shown to be effective in reducing the number of hidden units and the training time. To further improve the network's generalization capability, a smoothing operation is incorporated into the snowball training. Experimental results confirm the effectiveness of the approach.<<ETX>>","PeriodicalId":250037,"journal":{"name":"IEEE 1991 International Conference on Systems Engineering","volume":"283 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automatic rule generation for machine printed character recognition using multiple neural networks\",\"authors\":\"J. Wang, J. Jean\",\"doi\":\"10.1109/ICSYSE.1991.161148\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A set of neural networks is used in a two-stage character recognition system to resolve the confusion among similar characters. A snowball training algorithm is proposed to remedy the convergence problem encountered by backpropagation training. The algorithm is shown to be effective in reducing the number of hidden units and the training time. To further improve the network's generalization capability, a smoothing operation is incorporated into the snowball training. Experimental results confirm the effectiveness of the approach.<<ETX>>\",\"PeriodicalId\":250037,\"journal\":{\"name\":\"IEEE 1991 International Conference on Systems Engineering\",\"volume\":\"283 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE 1991 International Conference on Systems Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSYSE.1991.161148\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE 1991 International Conference on Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSYSE.1991.161148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic rule generation for machine printed character recognition using multiple neural networks
A set of neural networks is used in a two-stage character recognition system to resolve the confusion among similar characters. A snowball training algorithm is proposed to remedy the convergence problem encountered by backpropagation training. The algorithm is shown to be effective in reducing the number of hidden units and the training time. To further improve the network's generalization capability, a smoothing operation is incorporated into the snowball training. Experimental results confirm the effectiveness of the approach.<>
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