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Text effects are combinations of visual elements such as outlines, colors andtextures of text, which can dramatically improve its artistry. Although texteffects are extensively utilized in the design industry, they are usuallycreated by human experts due to their extreme complexity; this is laborious andnot practical for normal users. In recent years, some efforts have been madetoward automatic text effect transfer; however, the lack of data limits thecapabilities of transfer models. To address this problem, we introduce a newtext effects dataset, TE141K, with 141,081 text effect/glyph pairs in total.Our dataset consists of 152 professionally designed text effects rendered onglyphs, including English letters, Chinese characters, and Arabic numerals. Tothe best of our knowledge, this is the largest dataset for text effect transferto date. Based on this dataset, we propose a baseline approach called texteffect transfer GAN (TET-GAN), which supports the transfer of all 152 styles inone model and can efficiently extend to new styles. Finally, we conduct acomprehensive comparison in which 14 style transfer models are benchmarked.Experimental results demonstrate the superiority of TET-GAN both qualitativelyand quantitatively and indicate that our dataset is effective and challenging.

TE141K: Artistic Text Benchmark for Text Effect Transfer