A hierarchical deformation model for online cursive script recognition Abstract   For the character recognition problem, it is known that the prior knowledge about the structure of patterns can be utilized as a guidance to obtain an accurate match efficiently. Naturally, the strokes, which are the primitive components of Chinese characters, play an important role to guide the correct recognition. Based on this highlevel structural information, a hierarchical deformation model is proposed to describe the deformation of online cursive Chinese characters. The new approach consists of two levels of match processes. First, the attributed string editing algorithm matches two sequences of turn points extracted from the input and the reference characters to determine the stroke matches. Next, the constrained parabola transformation is used to reduce the difference between the matched strokes appropriately. Experimental results show that the hierarchical deformation model is a quite accurate approximation to the deformation of cursive Chinese characters with much lower computational cost. Furthermore, the distance measure between deformable characters derived in this paper is robust enough to greatly improve the performance of practical recognition systems.

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.