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A robot calligraphy writing method based on style transferring algorithm and similarity evaluation

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Abstract

This paper proposes a robot calligraphy writing method based on style transfer algorithm, which can transfer the art style of Chinese font and maintain the imitation accuracy of the calligraphy. A prototypical similarity evaluation approach is also presented to analyze the calligraphy writing effect, including the similarity ratio of balance, inclination and writing intensity. The robot writing system mainly consists of a six-DOF robot, manipulator, Chinese brush and PC controller. Firstly, input character images are transformed into target style font through training and style transferring process based on generative adversarial networks method. Then, the description parameters of the character images with target style are extracted to control the robot calligraphy writing process precisely. Experimental results show that the minimum similarity between the styles of robot writing characters and target characters is larger than 0.75. The proposed method shows good calligraphy writing effect, which can be used for various calligraphy training processes and trajectory planning applications.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (51805280), the Natural Science Foundation of Zhejiang province (LQ18E050005), the Ningbo Technology Innovation 2025 Project (2018B10005), Zhejiang Province Public Welfare Technology Application Research Project (2017C31094) and the K. C. Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering and Mechanics, Ningbo University, No. 818 Fenghua Road, Jiangbei District, Ningbo City, Zhejiang Province, China

    Dong-tai Liang, Dan Liang, Shu-min Xing, Ping Li & Xiao-cheng Wu

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  1. Dong-tai Liang
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  2. Dan Liang
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Correspondence to Dong-tai Liang.

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Liang, Dt., Liang, D., Xing, Sm. et al. A robot calligraphy writing method based on style transferring algorithm and similarity evaluation. Intel Serv Robotics 13, 137–146 (2020). https://doi.org/10.1007/s11370-019-00298-3

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