# Chinese Typography Transfer

arXiv: https://arxiv.org/abs/1707.04904 · HTML: https://mlsanity.com/paper/1707.04904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks](https://mlsanity.com/paper/2312.09673/) (2023)

## Abstract (arXiv)

In this paper, we propose a new network architecture for Chinese typography transformation based on deep learning. The architecture consists of two sub-networks: (1)a fully convolutional network(FCN) aiming at transferring specified typography style to another in condition of preserving structure information; (2)an adversarial network aiming at generating more realistic strokes in some details. Unlike models proposed before 2012 relying on the complex segmentation of Chinese components or strokes, our model treats every Chinese character as an inseparable image, so pre-processing or post-preprocessing are abandoned. Besides, our model adopts end-to-end training without pre-trained used in other deep models. The experiments demonstrates that our model can synthesize realistic-looking target typography from any source typography both on printed style and handwriting style.