Abstract
Glyphs in many writing systems (e.g., Chinese) are composed of a sequence of strokes written in a specific order. Glyph structure interpreting (i.e., stroke extraction) is one of the most important processing steps in many tasks including aesthetic quality evaluation, handwriting synthesis, character recognition, etc. However, existing methods that rely heavily on accurate shape matching are not only time-consuming but also unsatisfactory in stroke extraction performance. In this paper, we propose a novel method based on semantic segmentation and tabu search to interpret the structure of Chinese glyphs. Specifically, we first employ an improved Fully Convolutional Network (FCN), DeepStroke, to extract strokes, and then use the tabu search to obtain the order how these strokes are drawn. We also build the Chinese Character Stroke Segmentation Dataset (CCSSD) consisting of 67630 character images that can be equally classified into 10 different font styles. This dataset provides a benchmark for both stroke extraction and semantic segmentation tasks. Experimental results demonstrate the effectiveness and efficiency of our method and validate its superiority against the state of the art.
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Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput. Sci. 4, 357–361 (2014)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv preprint arXiv:1606.00915 (2016)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation (2018)
Chen, X., Lian, Z., Tang, Y., Xiao, J.: A benchmark for stroke extraction of Chinese characters. Acta Scientiarum Naturalium Universitatis Pekinensis 2, 4 (2016)
Lian, Z., Xiao, J.: Automatic shape morphing for Chinese characters. In: SIGGRAPH Asia 2012 Technical Briefs, p. 2. ACM (2012)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mottaghi, R., et al.: The role of context for object detection and semantic segmentation in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 891–898 (2014)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional models for semantic segmentation. TPAMI (2016)
Sun, Y., Qian, H., Xu, Y.: A geometric approach to stroke extraction for the Chinese calligraphy robot. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3207–3212. IEEE (2014)
Wang, C., Lian, Z., Tang, Y., Xiao, J.: Automatic correspondence finding for Chinese characters using graph matching. In: Seventh International Conference on Image and Graphics, pp. 545–550 (2013)
Wang, X., Liang, X., Sun, L., Liu, M.: Triangular mesh based stroke segmentation for Chinese calligraphy. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1155–1159. IEEE (2013)
Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684–3692 (2018)
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. arXiv preprint arXiv:1808.00897 (2018)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network (2016)
Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No.: 61672056 and 61672043) and Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology).
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Wang, W., Lian, Z., Tang, Y., Xiao, J. (2020). DeepStroke: Understanding Glyph Structure with Semantic Segmentation and Tabu Search. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_29
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DOI: https://doi.org/10.1007/978-3-030-37731-1_29
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