#Fonts style mods#

On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.',Ī Deep Factorization of Style and Structure in Fonts When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself.

Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. Publisher = 'Association for Computational Linguistics',Ībstract = 'We propose a deep factorization model for typographic analysis that disentangles content from style.

#Fonts style mods#

Cite (Informal): A Deep Factorization of Style and Structure in Fonts (Srivatsan et al., EMNLP 2019) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code = 'A Deep Factorization of Style and Structure in Fonts',īooktitle = 'Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)', Association for Computational Linguistics. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2195–2205, Hong Kong, China. A Deep Factorization of Style and Structure in Fonts. | IJCNLP SIG: SIGDAT Publisher: Association for Computational Linguistics Note: Pages: 2195–2205 Language: URL: DOI: 10.18653/v1/D19-1225 Bibkey: srivatsan-etal-2019-deep Cite (ACL): Nikita Srivatsan, Jonathan Barron, Dan Klein, and Taylor Berg-Kirkpatrick. Anthology ID: D19-1225 Original: D19-1225v1 Version 2: D19-1225v2 Volume: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) Month: November Year: 2019 Address: Hong Kong, China Venues: EMNLP

On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work. Abstract We propose a deep factorization model for typographic analysis that disentangles content from style.