TemplateInstance Loss for Offline Handwritten Chinese Character Recognition The longstanding challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner. In this paper, we propose the template and instance loss functions for the relevant machine learning tasks in offline handwritten Chinese character recognition. First, the character template is designed to deal with the intrinsic similarities among Chinese characters. Second, the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character. Trained with the new loss functions using our deep network architecture HCCR14Layer model consisting of simple layers, our extensive experiments show that it yields stateoftheart performance and beyond for offline HCCR.

Multiple Comparative Attention Network for Offline Handwritten Chinese Character Recognition Recent advances in deep learning have made great progress in offline Handwritten Chinese Character Recognition (HCCR). However, most existing CNNbased 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 subregions. Moreover, we apply a regionlevel center loss to pull the features that learned from the same class and different regions closer to further obtain robust features invariant to large intraclass 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 singlenetwork methods trained only on handwritten data.