# Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis

arXiv: https://arxiv.org/abs/1706.08789 · HTML: https://mlsanity.com/paper/1706.08789/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 29 of 29)

- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning](https://mlsanity.com/paper/2312.12142/) (2023)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [Few-Shot Font Generation by Learning Fine-Grained Local Styles](https://mlsanity.com/paper/2205.09965/) (2022)
- [Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator](https://mlsanity.com/paper/2205.00146/) (2022)
- [DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality](https://mlsanity.com/paper/2303.14585/) (2023)
- [CF-Font: Content Fusion for Few-shot Font Generation](https://mlsanity.com/paper/2303.14017/) (2023)
- [ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach](https://mlsanity.com/paper/2108.03596/) (2021)
- [TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design](https://mlsanity.com/paper/2308.04733/) (2023)
- [RZCR: Zero-shot Character Recognition via Radical-based Reasoning](https://mlsanity.com/paper/2207.05842/) (2022)
- [Impressions2Font: Generating Fonts by Specifying Impressions](https://mlsanity.com/paper/2103.10036/) (2021)
- [GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation](https://mlsanity.com/paper/2207.09649/) (2022)
- [VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization](https://mlsanity.com/paper/2308.14018/) (2023)
- [WAS: Dataset and Methods for Artistic Text Segmentation](https://mlsanity.com/paper/2408.00106/) (2024)
- [Font Style that Fits an Image -- Font Generation Based on Image Context](https://mlsanity.com/paper/2105.08879/) (2021)
- [Diff-Oracle: Deciphering Oracle Bone Scripts with Controllable Diffusion Model](https://mlsanity.com/paper/2312.13631/) (2023)
- [MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories](https://mlsanity.com/paper/2506.04807/) (2025)
- [DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2212.14742/) (2022)
- [MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation](https://mlsanity.com/paper/2503.02799/) (2025)
- [CalliRewrite: Recovering Handwriting Behaviors from Calligraphy Images without Supervision](https://mlsanity.com/paper/2405.15776/) (2024)
- [FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation](https://mlsanity.com/paper/2603.22054/) (2026)
- [ShufaNet: Classification method for calligraphers who have reached the professional level](https://mlsanity.com/paper/2111.11350/) (2021)
- [Natural Scene Text Editing Based on AI](https://mlsanity.com/paper/2111.15475/) (2021)
- [End-to-End Rubbing Restoration Using Generative Adversarial Networks](https://mlsanity.com/paper/2205.03743/) (2022)
- [StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding](https://mlsanity.com/paper/2211.06198/) (2022)
- [SGCE-Font: Skeleton Guided Channel Expansion for Chinese Font Generation](https://mlsanity.com/paper/2211.14475/) (2022)
- [Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks](https://mlsanity.com/paper/2312.09673/) (2023)
- [Text Image Generation for Low-Resource Languages with Dual Translation Learning](https://mlsanity.com/paper/2409.17747/) (2024)
- [HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space](https://mlsanity.com/paper/2606.18788/) (2026)

## Abstract (arXiv)

In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. We treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model.