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Abstract
The classical convolutional neural network has been widely used for handwritten digit character recognition with high accuracy. However, due to its small convolutional layer, fixed size of convolution kernel and few extracted features, the recognition accuracy of complex handwritten characters are reduced. In this paper, an improved deep convolutional neural network model is proposed, which can allocate different convolution kernels according to the different information amount in the handwritten character image area for convolution, so as to better extract the effective information of the image and is more suitable for complex handwritten character recognition applications. Experiments show that the recognition rate can be higher.
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Details
1 Xijing University, Xian, Shaanxi, 710123, China
2 Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China