The generation of stylish Chinese fonts is an important problem involved in
many applications. Most of existing generation methods are based on the deep
generative models, particularly, the generative adversarial networks (GAN)
based models. However, these deep generative models may suffer from the mode
collapse issue, which significantly degrades the diversity and quality of
generated results. In this paper, we introduce a one-bit stroke encoding to
capture the key mode information of Chinese characters and then incorporate it
into CycleGAN, a popular deep generative model for Chinese font generation. As
a result we propose an efficient method called StrokeGAN, mainly motivated by
the observation that the stroke encoding contains amount of mode information of
Chinese characters. In order to reconstruct the one-bit stroke encoding of the
associated generated characters, we introduce a stroke-encoding reconstruction
loss imposed on the discriminator. Equipped with such one-bit stroke encoding
and stroke-encoding reconstruction loss, the mode collapse issue of CycleGAN
can be significantly alleviated, with an improved preservation of strokes and
diversity of generated characters. The effectiveness of StrokeGAN is
demonstrated by a series of generation tasks over nine datasets with different
fonts. The numerical results demonstrate that StrokeGAN generally outperforms
the state-of-the-art methods in terms of content and recognition accuracies, as
well as certain stroke error, and also generates more realistic characters.

本论文提出了 StrokeGAN 算法，通过使用一位笔画编码和笔画编码重建损失函数来避免 CycleGAN 模型的模式崩溃问题，并在九个不同的字体数据集上展示了 StrokeGAN 相对于现有方法在字符生成方面的显着优势.