  This paper introduces the problem of automatic font pairing. Font pairing is
an important design task that is difficult for novices. Given a font selection
for one part of a document (e.g., header), our goal is to recommend a font to
be used in another part (e.g., body) such that the two fonts used together look
visually pleasing. There are three main challenges in font pairing. First, this
is a fine-grained problem, in which the subtle distinctions between fonts may
be important. Second, rules and conventions of font pairing given by human
experts are difficult to formalize. Third, font pairing is an asymmetric
problem in that the roles played by header and body fonts are not
interchangeable. To address these challenges, we propose automatic font pairing
through learning visual relationships from large-scale human-generated font
pairs. We introduce a new database for font pairing constructed from millions
of PDF documents available on the Internet. We propose two font pairing
algorithms: dual-space k-NN and asymmetric similarity metric learning (ASML).
These two methods automatically learn fine-grained relationships from
large-scale data. We also investigate several baseline methods based on the
rules from professional designers. Experiments and user studies demonstrate the
effectiveness of our proposed dataset and methods.
