Abstract
Calligraphy occupies a distinguished position in Chinese traditional culture. For long-term preservation, many calligraphy works have been carved on stones or woods in the form of relief. In this paper, we present a novel solution that enables fast modeling of Chinese calligraphy relief from 2D handwriting image, which benefits from the advances of deep learning. We first construct a relief dataset composed of diverse types of calligraphy fonts and then design a convolutional neural network for height predictions. Through the trained network, one can quickly generate homogeneous type, inhomogeneous type or hybrid style of reliefs. The advantage over previous methods is that our method does not require parameter tuning and is fast in generating calligraphy reliefs from different resolution of inputs. A number of experiments and comparisons prove the effectiveness of our method.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their careful reviews and valuable suggestions. This work was supported in part by the National Natural Science Foundation of China (No. 61772293), and the NSFC-Zhejiang Joint Fund of the Integration of Informatization and Industrialization (U1609218).
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Zhang, YW., Wang, J., Long, W. et al. A fast solution for Chinese calligraphy relief modeling from 2D handwriting image. Vis Comput 36, 2241–2250 (2020). https://doi.org/10.1007/s00371-020-01917-2
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DOI: https://doi.org/10.1007/s00371-020-01917-2