  Text effects are combinations of visual elements such as outlines, colors and
textures of text, which can dramatically improve its artistry. Although text
effects are extensively utilized in the design industry, they are usually
created by human experts due to their extreme complexity; this is laborious and
not practical for normal users. In recent years, some efforts have been made
toward automatic text effect transfer; however, the lack of data limits the
capabilities of transfer models. To address this problem, we introduce a new
text effects dataset, TE141K, with 141,081 text effect/glyph pairs in total.
Our dataset consists of 152 professionally designed text effects rendered on
glyphs, including English letters, Chinese characters, and Arabic numerals. To
the best of our knowledge, this is the largest dataset for text effect transfer
to date. Based on this dataset, we propose a baseline approach called text
effect transfer GAN (TET-GAN), which supports the transfer of all 152 styles in
one model and can efficiently extend to new styles. Finally, we conduct a
comprehensive comparison in which 14 style transfer models are benchmarked.
Experimental results demonstrate the superiority of TET-GAN both qualitatively
and quantitatively and indicate that our dataset is effective and challenging.
