A Novel Data Augmentation Method for Chinese Character Spatial Structure Recognition by Normalized Deformable Convolutional Networks

In this paper, we propose a novel data augmentation method and a normalized deformable convolutional network for natural image classification and handwritten Chinese character structure recognition. The spatial structure is the basic characteristics of Chinese character, and it plays a very important role in understanding and learning Chinese character. But the convolutional neural networks are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. So, we use the deformable convolutional network to deal with this task. Furthermore, we propose a normalized deformable convolutional network to improve the stability and accuracy of the model. Besides, some traditional data augmentation method could change one Chinese character structure to another, we propose a novel data augmentation method named Matt data augmentation (MDA) to improve the recognition performance. The normalized deformable Resnet with MDA achieve the best accuracy (93.62%) on handwritten Chinese character structure data set. Besides, the CapsuleNet with MDA can also improve to 89.41% test accuracy compared to without MDA (87.75%). Extensive experiments validate the performance of our approach.