Modeling Fonts in Context: Font Prediction on Web Designs

dc.contributor.authorZhao, Nanxuanen_US
dc.contributor.authorCao, Yingen_US
dc.contributor.authorLau, Rynson W. H.en_US
dc.contributor.editorFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannesen_US
dc.date.accessioned2018-10-07T15:00:18Z
dc.date.available2018-10-07T15:00:18Z
dc.date.issued2018
dc.description.abstractWeb designers often carefully select fonts to fit the context of a web design to make the design look aesthetically pleasing and effective in communication. However, selecting proper fonts for a web design is a tedious and time-consuming task, as each font has many properties, such as font face, color, and size, resulting in a very large search space. In this paper, we aim to model fonts in context, by studying a novel and challenging problem of predicting fonts that match a given web design. To this end, we propose a novel, multi-task deep neural network to jointly predict font face, color and size for each text element on a web design, by considering multi-scale visual features and semantic tags of the web design. To train our model, we have collected a CTXFont dataset, which consists of 1k professional web designs, with labeled font properties. Experiments show that our model outperforms the baseline methods, achieving promising qualitative and quantitative results on the font selection task. We also demonstrate the usefulness of our method in a font selection task via a user study.en_US
dc.description.number7
dc.description.sectionheaders2D and 2.5D Design
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13576
dc.identifier.issn1467-8659
dc.identifier.pages385-395
dc.identifier.urihttps://doi.org/10.1111/cgf.13576
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13576
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies → Perception
dc.subjectNeural networks
dc.titleModeling Fonts in Context: Font Prediction on Web Designsen_US
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