Abstract
Chinese characters are complex graphics with strokes as the basic unit. In order to analyze their structure, stroke extraction is the first step. This paper presents an automatic extraction method of Chinese character strokes, which regards the extraction of Chinese character strokes as finding the optimal path and merging. Based on the superpixel network, the path network is applied to enumerate all possible stroke segments. Then, the repeated strokes are merged according to the Intersection over Union. Experimental results show that the method can effectively extract accurate strokes and obtain single strokes with high-level semantic features and complete information.
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References
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Bi, F., Han, J., Tian, Y., Wang, Y.: Ssgan: generative adversarial networks for the stroke segmentation of calligraphic characters. Vis. Comput. 1–10 (2021). https://doi.org/10.1007/s00371-021-02133-2
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient nd image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Chen, W., Sui, L., Xu, Z., Lang, Y.: Improved Zhang-Suen thinning algorithm in binary line drawing applications. In: 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 1947–1950. IEEE (2012)
Fang, L., Yunyang, Z.: Research on a Tibetan image refinement algorithm based on adjacent pixel points information. Comput. Technol. Dev. 28(4), 21–24 (2018)
Fucheng, Z.: Research on Calligraphy Style Recognition Based on Convolutional Neural Network. Master’s thesis, Xi’an University of Technology (2018)
Kim, B., Wang, O., Öztireli, A.C., Gross, M.: Semantic segmentation for line drawing vectorization using neural networks. In: Computer Graphics Forum, vol. 37, pp. 329–338. Wiley Online Library (2018)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rui, C., Yan, T.: An improved stroke extraction model for Chinese character and its implementation. Comput. Sci 31(12) (2004)
Rui, C., Yan, T.: A stroke extraction model for Chinese character. Department of Computer Science 31(12), 164 (2004)
Wenlu, Y., Yehao, W., Hong, X.: An algorithm of stroke separation based on TTF vector font outline. Comput. Sci. 29 (2019)
Xiafen, Z., Jiayan, L.: Extracting Chinese calligraphy strokes using stroke crawler. J. Comput. Aided Des. Graph. 2, 301–309 (2016)
Xinwei, Z., Changqiang, Y.: Graph based stroke extraction for Chinese calligraphy. Softw. Guide 18(4), 184–187 (2019)
Xu, S., Lau, F.C., Cheung, W.K., Pan, Y.: Automatic generation of artistic Chinese calligraphy. IEEE Intell. Syst. 20(3), 32–39 (2005)
Yang, Z.: Brush stroke extraction based on BP neural network. Ph.D. thesis, Shandong University of science and technology (2018)
Zhang, J.S., Yu, J.H., Mao, G.H., Ye, X.Z.: Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes. J. Zhejiang Univ. Sci. A 7(7), 1178–1186 (2006). https://doi.org/10.1631/jzus.2006.A1178
Acknowledgement
This work was supported in part by the University Innovation Team Project of Jinan (2019GXRC015), and in part by Key Science & Technology Innovation Project of Shandong Province (2019JZZY010324).
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Shu, J., Chen, Y., Cao, Y., Zhao, Y. (2021). Extraction and Merging of Stroke Structure of Chinese Characters. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13017. Springer, Cham. https://doi.org/10.1007/978-3-030-90439-5_13
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DOI: https://doi.org/10.1007/978-3-030-90439-5_13
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