FlexyFont: Learning Transferring Rules for Flexible Typeface Synthesis

Maintaining consistent styles across glyphs is an arduous task in typeface design. In this work we introduce FlexyFont, a flexible tool for synthesizing a complete typeface that has a consistent style with a given small set of glyphs. Motivated by a key fact that typeface designers often maintain a...

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Vydané v:	Computer graphics forum Ročník 34; číslo 7; s. 245 - 256
Hlavní autori:	Phan, H. Q., Fu, H., Chan, A. B.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.10.2015
Predmet:	Assembling Categories and Subject Descriptors (according to ACM CCS) Computer science Consistency Design Fonts I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation Image processing systems Learning Mathematical analysis Studies Tasks Type fonts Typefaces Vectors (mathematics) United States > US
ISSN:	0167-7055, 1467-8659
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Abstract	Maintaining consistent styles across glyphs is an arduous task in typeface design. In this work we introduce FlexyFont, a flexible tool for synthesizing a complete typeface that has a consistent style with a given small set of glyphs. Motivated by a key fact that typeface designers often maintain a library of glyph parts to achieve a consistent typeface, we intend to learn part consistency between glyphs of different characters across typefaces. We take a part assembling approach by firstly decomposing the given glyphs into semantic parts and then assembling them according to learned sets of transferring rules to reconstruct the missing glyphs. To maintain style consistency, we represent the style of a font as a vector of pairwise part similarities. By learning a distribution over these feature vectors, we are able to predict the style of a novel typeface given only a few examples. We utilize a popular machine learning method as well as retrieval‐based methods to quantitatively assess the performance of our feature vector, resulting in favorable results. We also present an intuitive interface that allows users to interactively create novel typefaces with ease. The synthesized fonts can be directly used in real‐world design.
AbstractList	Maintaining consistent styles across glyphs is an arduous task in typeface design. In this work we introduce FlexyFont, a flexible tool for synthesizing a complete typeface that has a consistent style with a given small set of glyphs. Motivated by a key fact that typeface designers often maintain a library of glyph parts to achieve a consistent typeface, we intend to learn part consistency between glyphs of different characters across typefaces. We take a part assembling approach by firstly decomposing the given glyphs into semantic parts and then assembling them according to learned sets of transferring rules to reconstruct the missing glyphs. To maintain style consistency, we represent the style of a font as a vector of pairwise part similarities. By learning a distribution over these feature vectors, we are able to predict the style of a novel typeface given only a few examples. We utilize a popular machine learning method as well as retrieval-based methods to quantitatively assess the performance of our feature vector, resulting in favorable results. We also present an intuitive interface that allows users to interactively create novel typefaces with ease. The synthesized fonts can be directly used in real-world design.
Author	Phan, H. Q. Fu, H. Chan, A. B.
Author_xml	– sequence: 1 givenname: H. Q. surname: Phan fullname: Phan, H. Q. organization: School of Creative Media, City University of Hong Kong – sequence: 2 givenname: H. surname: Fu fullname: Fu, H. organization: School of Creative Media, City University of Hong Kong – sequence: 3 givenname: A. B. surname: Chan fullname: Chan, A. B. organization: Department of Computer Science, City University of Hong Kong
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Snippet	Maintaining consistent styles across glyphs is an arduous task in typeface design. In this work we introduce FlexyFont, a flexible tool for synthesizing a...
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Title	FlexyFont: Learning Transferring Rules for Flexible Typeface Synthesis
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