Abstract
Various fonts give us various impressions, which are often represented by words. This paper proposes Impressions2Font (Imp2Font) that generates font images with specific impressions. Imp2Font is an extended version of conditional generative adversarial networks (GANs). More precisely, Imp2Font accepts an arbitrary number of impression words as the condition to generate the font images. These impression words are converted into a soft-constraint vector by an impression embedding module built on a word embedding technique. Qualitative and quantitative evaluations prove that Imp2Font generates font images with higher quality than comparative methods by providing multiple impression words or even unlearned words.
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Notes
- 1.
In this paper, we use the term “impression” in a broader meaning; some impression is described by words that relate more to font shapes, such as sans-serif, rather than subjective impression.
- 2.
These two differences make it very difficult to fairly compare Wang et al. [20] and our proposed method.
- 3.
As noted later, each impression word is converted to a semantic vector by word2vec [13]. Therefore, we remove too rare impression words that are not included even in the 3-million English vocabulary for training word2vec. This results in \(K=1,574\) impression words that we used in the following. Note that an impression word with hyphenation is split into sub-words, and then its semantic vector is derived by taking the sum of the semantic vectors of the sub-words.
- 4.
“HERONS” is a common word to check the font style since it contains sufficient variations of stroke shapes.
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This work was supported by JSPS KAKENHI Grant Number JP17H06100.
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Matsuda, S., Kimura, A., Uchida, S. (2021). Impressions2Font: Generating Fonts by Specifying Impressions. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_48
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