Multiple Comparative Attention Network for Offline Handwritten Chinese Character Recognition

Abstract

Recent advances in deep learning have made great progress in offline Handwritten Chinese Character Recognition (HCCR). However, most existing CNN-based methods only utilize global image features as contextual guidance to classify characters, while neglecting the local discriminative features which is very important for HCCR. To overcome this limitation, in this paper, we present a convolutional neural network with multiple comparative attention (MCANet) in order to produce separable local attention regions with discriminative feature across different categories. Concretely, our MCANet takes the last convolutional feature map as input and outputs multiple attention maps, a contrastive loss is used to restrict different attention selectively focus on different sub-regions. Moreover, we apply a region-level center loss to pull the features that learned from the same class and different regions closer to further obtain robust features invariant to large intra-class variance. Combining with classification loss, our method can learn which parts of images are relevant for recognizing characters and adaptively integrates information from different regions to make the final prediction. We conduct experiments on ICDAR2013 offline HCCR competition dataset with our proposed approach and achieves an accuracy of 97.66%, outperforming all single-network methods trained only on handwritten data.