Data augmentation and directional feature maps extraction for in-air handwritten Chinese character recognition based on convolutional neural network

Recently convolutional neural networks (CNN) have demonstrated remarkable performance in various classification problems. In this paper, we also introduce CNN into in-air handwritten Chinese character recognition (IAHCCR) and propose new directional feature maps, named bend directional feature maps. Then we integrate the combination of various types of directional feature maps with the CNN and obtain better recognition performance compared with other methods reported for IAHCCR. For further improving recognition rate, we propose a new data augmentation method dedicated to in-air handwritten Chinese characters. The proposed data augmentation method combines global transformation with local distortion and effectively enlarges the training dataset. Experimental results demonstrate that our proposed methods can greatly improve the recognition rate for IAHCCR.