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Graph-matching-based character recognition for Chinese seal images

Abstract

Recognizing characters in Chinese seal images is important when researching ancient cultural artworks because the seals may contain critical historical information. However, owing to large intraclass variance and a limited number of training samples, recognizing such characters in Chinese seals is challenging. Thus, this study proposes a graph-matching-based method to recognize characters in historical Chinese seal images. In the proposed method, a Chinese seal character is first modeled as a graph representing its underlying geometric structure. Then, two affinity matrices that measure the similarity of nodes and edge pairs are calculated with their local features. Finally, a correspondence matrix is calculated using a graph matching algorithm and the most similar reference is selected as the recognition result. Compared with several existing classification methods for seal image recognition, the proposed graph-matching-based method achieves better results, particularly in the case of limited samples.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 6152010-6001, 61801178), Natural Science Foundation of Hunan Province (Grant No. 2018JJ3071), and by Hunan Key Laboratory of Visual Perception and Artificial Intelligence.

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Correspondence to Shutao Li.

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Sun, B., Hua, S., Li, S. et al. Graph-matching-based character recognition for Chinese seal images. Sci. China Inf. Sci. 62, 192102 (2019). https://doi.org/10.1007/s11432-018-9724-7

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Keywords

  • graph model
  • Chinese seal
  • character recognition
  • graph matching