Performance Analysis Of Neural Network Handwritten Character Recognition System Using Cnn Edge Detection

dc.authoridTR125976en_US
dc.authoridTR26113en_US
dc.contributor.authorGorgel, Pelin
dc.contributor.authorN. Ucan, Osman
dc.date.accessioned2015-04-01T13:12:10Z
dc.date.available2015-04-01T13:12:10Z
dc.date.issued2007
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractIn this paper we have recognized the nearly 1200 Latin handwritten characters collected from people using artificial neural network. We used backpropagation algorithm for supervised learning. In pre-processing and feature extraction step, normalization and edge detection has been performed. Cellular neural network (CNN) is used for edge detection. CNN are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. In this system we achieved 84.5% recognition accuracy. To reach this percentage it is observed with graphics how input datas, network parametres and training period affect the result. Then the character recognition performance of the network according to changable parameters is analysed. And factors that increase performance of system are determined.en_US
dc.identifier.citationJournal of Science and Technology 1 (1), 2007, 94-105tr_TR
dc.identifier.issn1307-3818
dc.language.isoenen_US
dc.publisherBeykent Üniversitesitr_TR
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectCharacter Recognitiontr_TR
dc.subjectArtificial Neural Networkstr_TR
dc.subjectBackpropagationtr_TR
dc.subjectCellular Neural Networkstr_TR
dc.titlePerformance Analysis Of Neural Network Handwritten Character Recognition System Using Cnn Edge Detectionen_US
dc.typeArticleen_US

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