Handwritten digit recognition based on depth neural network

Abstract

Neural network and depth learning have been widely used in the field of image processing. Good recognition results are often required for complex network models. But the complex network model makes training difficult and takes a long time. In order to obtain a higher recognition rate with a simple model, the BP neural network and the convolutional neural network are studied separately and verified on the MNIST data set. In order to improve the recognition results further, a combined depth network is proposed and validated on the MNIST dataset. The experimental results show that the recognition effect of the combined depth network is obviously better than that of a single network. A more accurate recognition result is achieved by the combined network.