Abstract
Handwriting imitation is a challenging and interesting deep learning topic. This paper proposed a method to imitate handwriting style by style transfer. We proposed an neural network model based on conditional generative adversarial networks (cGAN) for handwriting style transfer. This paper improved the loss function on the basis of the GAN. Compared with other handwriting imitation methods, the handwriting style transfer’s effect and efficiency have been significantly improved. The experiments showed that the shape of the generated Chinese characters is clear and the analysis of experimental data showed the Generative adversarial networks showed excellent performance in handwriting style transfer. The generated text image is closer to the real handwriting and achieved a better performance in term of handwriting imitation.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (61772179), Hunan Provincial Natural Science Foundation of China (2020JJ4152, 2019JJ40005), the Science and Technology Plan Project of Hunan Province(2016TP1020), Double First-Class University Project of Hunan Province (Xiangjiaotong [2018]469), Postgraduate Scientific Research Innovation Project of Hunan Province (CX20190998), Degree & Postgraduate Education Reform Project of Hunan Province (2019JGYB266, 2020JGZD072), Industry University Research Innovation Foundation of Ministry of Education Science and Technology Development Center (2020QT09), Hengyang technology innovation guidance projects (Hengcaijiaozhi [2020]-67), Postgraduate Teaching Platform Project of Hunan Province (Xiangjiaotong [2019]370-321).
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Yang, K., Liang, X., Liu, Q., Wen, K. (2022). Handwriting Imitation with Generative Adversarial Networks. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_22
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DOI: https://doi.org/10.1007/978-981-16-6554-7_22
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