Handwritten Chinese Character Recognition by Joint Classification and Similarity Ranking

Deep convolutional neural networks (DCNN) have recently achieved state-of-the-art performance on handwritten Chinese character recognition (HCCR). However, most of DCNN models employ the softmax activation function and minimize cross-entropy loss, which may loss some inter-class information. To cope with this problem, we demonstrate a small but consistent advantage of using both classification and similarity ranking signals as supervision. Specifically, the presented method learns a DCNN model by maximizing the inter-class variations and minimizing the intra-class variations, and simultaneously minimizing the cross-entropy loss. In addition, we also review some loss functions for similarity ranking and evaluate their erformance. Our experiments demonstrate that the presented method achieves state-of-the-art accuracy on the well-known ICDAR 2013 offline HCCR competition dataset.