This paper presents a probabilistic approach to recognize handwritten Chinese characters. According to the stroke writing sequence, strokes and interleaved stroke relations are built manually as a 1D string, called online models, to describe a Chinese character. The recognition problem is formulated as an optimization process in a multistage directed graph, where the number of stages is the length of the modelled stroke sequence. Nodes in a stage represent extracted strokes. The Viterbi algorithm, which can handle stroke insertion, deletion, splitting, and merging, is applied to compute the similarity between each modelled character and the unknown character. The unknown character is recognized as the one with the highest similarity. Experiments with 500 characters uniformly selected from the database CCL/HCCR1 are conducted, and the recognition rate is about 94.3%.
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