Research on Residual Convolutional Neural Network for Handwritten Digit Recognition

Abstract

The technology of handwritten digit recognition has been widely applied in various situations and has significant practical significance. However, the morphological features of handwritten numbers are very complex, and achieving accurate recognition of handwritten numbers relies on efficient and accurate recognition techniques. This article proposes a residual convolutional network model to address the issues of inaccurate feature extraction and weak model generalization ability in convolutional neural networks. By introducing residual blocks into the network, the problem of vanishing and exploding network gradients is effectively eliminated. At the same time, the Batch Normalization and Dropout layers are introduced to accelerate the network training process and reduce the risk of overfitting. Finally, the k-fold cross validation method was used to select the optimal parameter configuration of the model. The experimental results show that residual convolutional neural networks have the characteristics of high recognition accuracy and strong model generalization ability.