  The generation of Chinese fonts has a wide range of applications. The
currently predominated methods are mainly based on deep generative models,
especially the generative adversarial networks (GANs). However, existing
GAN-based models usually suffer from the well-known mode collapse problem. When
mode collapse happens, the kind of GAN-based models will be failure to yield
the correct fonts. To address this issue, we introduce a one-bit stroke
encoding and a few-shot semi-supervised scheme (i.e., using a few paired data
as semi-supervised information) to explore the local and global structure
information of Chinese characters respectively, motivated by the intuition that
strokes and characters directly embody certain local and global modes of
Chinese characters. Based on these ideas, this paper proposes an effective
model called \textit{StrokeGAN+}, which incorporates the stroke encoding and
the few-shot semi-supervised scheme into the CycleGAN model. The effectiveness
of the proposed model is demonstrated by amounts of experiments. Experimental
results show that the mode collapse issue can be effectively alleviated by the
introduced one-bit stroke encoding and few-shot semi-supervised training
scheme, and that the proposed model outperforms the state-of-the-art models in
fourteen font generation tasks in terms of four important evaluation metrics
and the quality of generated characters. Besides CycleGAN, we also show that
the proposed idea can be adapted to other existing models to improve their
performance. The effectiveness of the proposed model for the zero-shot
traditional Chinese font generation is also evaluated in this paper.
