Network architecture exploration for Chinese character recognition with densenet

Abstract

Chinese character recognition can be widely used in many fields. Though Resnet and Densenet are both used in this area already, using these two networks and making a comparison on training performances between them is a ground that has not been explored. In this paper, these two methods are built and compared. Firstly, a dataset of Chinese character including 5,772 images with 28*28 size will be introduced. Next, Resnet and Densenet model pre-trained on the dataset is selected. Then fine-tuning is done to improve the accuracy of networks. After 50 epochs of training, the final result shows that Densenet is more stable compared with Resnet but less efficient with more epoches to perform well.