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Performance Analysis Of Neural Network Handwritten Character Recognition System Using Cnn Edge Detection
(Beykent Üniversitesi, 2007) Gorgel, Pelin; N. Ucan, Osman
In 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.