M. Gioiello, F. Sorbello, A. Tarantino, G. Vassallo
{"title":"一种高效的字符识别数字体系结构","authors":"M. Gioiello, F. Sorbello, A. Tarantino, G. Vassallo","doi":"10.1109/CAMP.1995.521031","DOIUrl":null,"url":null,"abstract":"We introduce a new digital neural architecture designed for automatic hand-written characters recognition. The architecture implements a two-layer perceptron off-line trained by conjugate gradient descent algorithm and the final weights are quantized and stored in a RAM. The architecture was developed and tested using the VHDL Alliance 2.0 CAD System simulator: it is easy to implement using standard VLSI technologies and may be used to deal with multi-level inputs.","PeriodicalId":277209,"journal":{"name":"Proceedings of Conference on Computer Architectures for Machine Perception","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An efficient digital architecture for character recognition\",\"authors\":\"M. Gioiello, F. Sorbello, A. Tarantino, G. Vassallo\",\"doi\":\"10.1109/CAMP.1995.521031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a new digital neural architecture designed for automatic hand-written characters recognition. The architecture implements a two-layer perceptron off-line trained by conjugate gradient descent algorithm and the final weights are quantized and stored in a RAM. The architecture was developed and tested using the VHDL Alliance 2.0 CAD System simulator: it is easy to implement using standard VLSI technologies and may be used to deal with multi-level inputs.\",\"PeriodicalId\":277209,\"journal\":{\"name\":\"Proceedings of Conference on Computer Architectures for Machine Perception\",\"volume\":\"150 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Conference on Computer Architectures for Machine Perception\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMP.1995.521031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Conference on Computer Architectures for Machine Perception","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMP.1995.521031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An efficient digital architecture for character recognition
We introduce a new digital neural architecture designed for automatic hand-written characters recognition. The architecture implements a two-layer perceptron off-line trained by conjugate gradient descent algorithm and the final weights are quantized and stored in a RAM. The architecture was developed and tested using the VHDL Alliance 2.0 CAD System simulator: it is easy to implement using standard VLSI technologies and may be used to deal with multi-level inputs.
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