Automated Chinese handwriting error detection using attributed relational graph matching

Abstract

Due to the complex shapes and various writing styles of Chinese characters, it is a challenge to automatically detect the errors in people's handwriting. In this paper, we use attributed relational graph to represent a Chinese character. To model the spatial relationships between the strokes in a Chinese character, a refined interval relationship that considers more granular levels is proposed. A novel interval neighborhood graph is also proposed to compute the distances among the refined interval relationships. Error-tolerant graph matching is used to locate the stroke production errors, sequence error as well as the spatial relationship errors. We also propose a pruning strategy in order to speed up the graph matching. Experiment results show that our proposed method outperforms existing approaches in terms of accuracy as well as its ability to handle more kinds of handwriting errors in less computational time. © 2008 Springer-Verlag Berlin Heidelberg.