  The long-standing 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 state-of-the-art performance and
beyond for offline HCCR.
