  Font selection is one of the most important steps in a design workflow.
Traditional methods rely on ordered lists which require significant domain
knowledge and are often difficult to use even for trained professionals. In
this paper, we address the problem of large-scale tag-based font retrieval
which aims to bring semantics to the font selection process and enable people
without expert knowledge to use fonts effectively. We collect a large-scale
font tagging dataset of high-quality professional fonts. The dataset contains
nearly 20,000 fonts, 2,000 tags, and hundreds of thousands of font-tag
relations. We propose a novel generative feature learning algorithm that
leverages the unique characteristics of fonts. The key idea is that font images
are synthetic and can therefore be controlled by the learning algorithm. We
design an integrated rendering and learning process so that the visual feature
from one image can be used to reconstruct another image with different text.
The resulting feature captures important font design details while is robust to
nuisance factors such as text. We propose a novel attention mechanism to
re-weight the visual feature for joint visual-text modeling. We combine the
feature and the attention mechanism in a novel recognition-retrieval model.
Experimental results show that our method significantly outperforms the
state-of-the-art for the important problem of large-scale tag-based font
retrieval.
