Graph Model Optimization based Historical Chinese Character Segmentation Method

Historical Chinese document recognition technology is important for digital library. However, historical Chinese character segmentation remains a difficult problem due to the complex structure of Chinese characters and various writing styles. This paper presents a novel method for historical Chinese character segmentation based on graph model. After a preliminary over-segmentation stage, the system applies a merging process. The candidate segmentation positions are denoted by the nodes of a graph, and the merging process is regarded as selecting an optimal path of the graph. The weight of edge in the graph is calculated by the cost function which considers geometric features and recognition confidence. Experimental results show that the proposed method is effective with a detection rate of 94.6% and an accuracy rate of 96.1% on a test set of practical historical Chinese document samples.