# Arbitrary Handwriting Image Style Transfer

arXiv: https://arxiv.org/abs/2201.05346 · HTML: https://mlsanity.com/paper/2201.05346/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Controlling Perceptual Factors in Neural Style Transfer](https://mlsanity.com/paper/1611.07865/) (2016)
- [Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks](https://mlsanity.com/paper/1708.09641/) (2017)

## Abstract (arXiv)

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.