Learning A Stroke‐Based Representation for Fonts

dc.contributor.authorBalashova, Elenaen_US
dc.contributor.authorBermano, Amit H.en_US
dc.contributor.authorKim, Vladimir G.en_US
dc.contributor.authorDiVerdi, Stephenen_US
dc.contributor.authorHertzmann, Aaronen_US
dc.contributor.authorFunkhouser, Thomasen_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2019-03-17T09:56:59Z
dc.date.available2019-03-17T09:56:59Z
dc.date.issued2019
dc.description.abstractDesigning fonts and typefaces is a difficult process for both beginner and expert typographers. Existing workflows require the designer to create every glyph, while adhering to many loosely defined design suggestions to achieve an aesthetically appealing and coherent character set. This process can be significantly simplified by exploiting the similar structure character glyphs present across different fonts and the shared stylistic elements within the same font. To capture these correlations, we propose learning a stroke‐based font representation from a collection of existing typefaces. To enable this, we develop a stroke‐based geometric model for glyphs, a fitting procedure to reparametrize arbitrary fonts to our representation. We demonstrate the effectiveness of our model through a manifold learning technique that estimates a low‐dimensional font space. Our representation captures a wide range of everyday fonts with topological variations and naturally handles discrete and continuous variations, such as presence and absence of stylistic elements as well as slants and weights. We show that our learned representation can be used for iteratively improving fit quality, as well as exploratory style applications such as completing a font from a subset of observed glyphs, interpolating or adding and removing stylistic elements in existing fonts.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13540
dc.identifier.issn1467-8659
dc.identifier.pages429-442
dc.identifier.urihttps://doi.org/10.1111/cgf.13540
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13540
dc.publisher© 2019 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjecttypography
dc.subjectcurves & surfaces
dc.subjectComputing methodologies∼Shape analysis
dc.titleLearning A Stroke‐Based Representation for Fontsen_US
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