  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.
