Abstract
Radical-based methods for Chinese character recognition (CCR) have been proven effective and offer substantial advantages. Different from character-based methods, Chinese characters are described as combinations of structures and radicals, and character recognition is achieved by the proper identifications of these components. However, there are visual similarities among radicals, leading to the ambiguity problem for CCR, which is not fully utilized in previous work. Accordingly, in this study, we first employ the stroke order information of Chinese radicals to establish a radical similarity metric. Then we improve the radical-based CCR in two ways. During the training stage, we propose a new loss function called minimum Bayesian risk (MBR) based on the radical similarity metric to yield better performance. During the recognition stage, the radical similarity is adopted to post-correct the potential error recognition results, offering a low-cost yet effective solution. Experimental results on different radical-based CCR models and datasets demonstrate the effectiveness and robustness of our proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bickel, P.J., Doksum, K.A.: Mathematical statistics: basic ideas and selected topics, volumes I-II package. CRC Press (2015)
Cao, Z., Lu, J., Cui, S., Zhang, C.: Zero-shot handwritten Chinese character recognition with hierarchical decomposition embedding. Pattern Recogn. 107, 107488 (2020)
Casey, R., Nagy, G.: Recognition of printed Chinese characters. IEEE Trans. Electron. Comput. 1, 91–101 (1966)
Chen, J., Li, B., Xue, X.: Zero-shot Chinese character recognition with stroke-level decomposition (2021). arXiv preprint arXiv:2106.11613
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19–67 (2005)
Freitag, M., Grangier, D., Tan, Q., Liang, B.: High quality rather than high model probability: minimum bayes risk decoding with neural metrics. Trans. Assoc. Comput. Linguist. 10, 811–825 (2022)
Goel, V., Byrne, W.J.: Minimum bayes-risk automatic speech recognition. Comput. Speech Lang. 14(2), 115–135 (2000)
Goodman, J.: Parsing algorithms and metrics (1996). arXiv preprint cmp-lg/9605036
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.: DenseNet: Implementing efficient convnet descriptor pyramids (2014). arXiv preprint arXiv:1404.1869
Jiang, X., et al.: Group, contrast and recognize: a self-supervised method for chinese character recognition. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds.) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. LNCS, vol. 14190. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41685-9_26
Jin, L.W., Yin, J.X., Gao, X., Huang, J.C.: Study of several directional feature extraction methods with local elastic meshing technology for HCCR. In: Proceedings of the Sixth International Conference for Young Computer Scientist, pp. 232–236 (2001)
Kaiser, J., Horvat, B., Kacic, Z.: A novel loss function for the overall risk criterion based discriminative training of hmm models. In: INTERSPEECH, pp. 887–890 (2000)
Kingsbury, B.: Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3761–3764. IEEE (2009)
Kumar, S., Byrne, B.: Minimum bayes-risk word alignments of bilingual texts. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 140–147 (2002)
Kumar, S., Byrne, B.: Minimum bayes-risk decoding for statistical machine translation. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004, pp. 169–176 (2004)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recogn. 46(1), 155–162 (2013)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Morioka, T.: Integration of a Chinese character ontology and historical glyph examples. In: Proceedings of the 9th International Conference of Digital Archives and Digital Humanities (DADH2018), pp. 287–300. Organizer of 9th International Conference of Digital Archives and Digital (2018)
Ristad, E.S., Yianilos, P.N.: Learning string-edit distance. IEEE Trans. Pattern Anal. Mach. Intell. 20(5), 522–532 (1998)
Romera-Paredes, B., Torr, P.: An embarrassingly simple approach to zero-shot learning. In: International Conference on Machine Learning, pp. 2152–2161. PMLR (2015)
Su, Y.M., Wang, J.F.: A novel stroke extraction method for Chinese characters using Gabor filters. Pattern Recogn. 36(3), 635–647 (2003)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, T., Xie, Z., Li, Z., Jin, L., Chen, X.: Radical aggregation network for few-shot offline handwritten Chinese character recognition. Pattern Recogn. Lett. 125, 821–827 (2019)
Wang, W., Zheng, V.W., Yu, H., Miao, C.: A survey of zero-shot learning: settings, methods, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–37 (2019)
Wang, W., Zhang, J., Du, J., Wang, Z.R., Zhu, Y.: DenseRAN for offline handwritten Chinese character recognition. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 104–109. IEEE (2018)
Xiao, Y., Meng, D., Lu, C., Tang, C.K.: Template-instance loss for offline handwritten Chinese character recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 315–322. IEEE (2019)
Xue, M., Du, J., Zhang, J., Wang, Z.-R., Wang, B., Ren, B.: Radical composition network for Chinese character generation. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 252–267. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86549-8_17
Yang, C., Wang, Q., Du, J., Zhang, J., Wu, C., Wang, J.: A transformer-based radical analysis network for Chinese character recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3714–3719. IEEE (2021)
Yang, H.M., Zhang, X.Y., Yin, F., Liu, C.L.: Robust classification with convolutional prototype learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3474–3482 (2018)
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1464–1470. IEEE (2013)
Yujian, L., Bo, L.: A normalized Levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1091–1095 (2007)
Zeiler, M.D.: ADADELTA: an adaptive learning rate method (2012). arXiv preprint arXiv:1212.5701
Zeng, J., Xu, R., Wu, Y., Li, H., Lu, J.: STAR: Zero-shot Chinese character recognition with stroke-and radical-level decompositions (2022). arXiv preprint arXiv:2210.08490
Zhang, J., Du, J., Dai, L.: Radical analysis network for learning hierarchies of Chinese characters. Pattern Recogn. 103, 107305 (2020)
Zhang, X.Y., Bengio, Y., Liu, C.L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recogn. 61, 348–360 (2017)
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)
Zu, X., Yu, H., Li, B., Xue, X.: Chinese character recognition with augmented character profile matching. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6094–6102 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Han, Z., Du, J., Xue, M., Ma, J., Hu, P., Zhang, Z. (2024). Radical Similarity Based Model Optimization and Post-correction for Chinese Character Recognition. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14804. Springer, Cham. https://doi.org/10.1007/978-3-031-70533-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-70533-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70532-8
Online ISBN: 978-3-031-70533-5
eBook Packages: Computer ScienceComputer Science (R0)