{"title":"基于多字体基本特征的人工神经网络字符识别","authors":"E. Neves, A. Gonzaga, A. Slaets","doi":"10.1109/CYBVIS.1996.629463","DOIUrl":null,"url":null,"abstract":"Neural networks present an alternative approach for the character recognition problem. This paper describes the development of a recognition system of multi-font character using topological feature extraction to recognize capital isolated letters. By properly specifying a set of features such as vertical, horizontal, and slant strokes, curvature, open and closed areas, called here \"fundamental features\", the recognition was performed using a backpropagation neural network.","PeriodicalId":103287,"journal":{"name":"Proceedings II Workshop on Cybernetic Vision","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A multi-font character recognition based on its fundamental features by artificial neural networks\",\"authors\":\"E. Neves, A. Gonzaga, A. Slaets\",\"doi\":\"10.1109/CYBVIS.1996.629463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neural networks present an alternative approach for the character recognition problem. This paper describes the development of a recognition system of multi-font character using topological feature extraction to recognize capital isolated letters. By properly specifying a set of features such as vertical, horizontal, and slant strokes, curvature, open and closed areas, called here \\\"fundamental features\\\", the recognition was performed using a backpropagation neural network.\",\"PeriodicalId\":103287,\"journal\":{\"name\":\"Proceedings II Workshop on Cybernetic Vision\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings II Workshop on Cybernetic Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBVIS.1996.629463\",\"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 II Workshop on Cybernetic Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBVIS.1996.629463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-font character recognition based on its fundamental features by artificial neural networks
Neural networks present an alternative approach for the character recognition problem. This paper describes the development of a recognition system of multi-font character using topological feature extraction to recognize capital isolated letters. By properly specifying a set of features such as vertical, horizontal, and slant strokes, curvature, open and closed areas, called here "fundamental features", the recognition was performed using a backpropagation neural network.