Spatio-temporal unified model for on-line handwritten Chinese character recognition

This paper presents a novel spatio-temporal modeling method for on-line handwritten Chinese character recognition. In this method, a statistical structure model (SSM) is used to describe the structural feature of Chinese characters from a probabilistic aspect, and an improved hidden Markov model (PCHMM) is employed to capture temporal information contained in ink. These two models are combined closely leading to a powerful spatio-temporal unified model (STUM), which has shown strong description ability and resulted in superior performance in the experiments where traditional models such as HMM (Hidden Markov Model) and ARG (Attributed Relational Graph) are also introduced and compared