Exploratory Font Selection Using Crowdsourced Attributes

Peter O'Donovan1*     Jānis Lībeks1*     Aseem Agarwala2     Aaron Hertzmann1,2    
1University of Toronto    2Adobe
*Both authors contributed equally

attrselect attribute similarity

Try the Interfaces:

Attribute Interface

Group Interface



This paper presents interfaces for exploring large collections of fonts for design tasks. Existing interfaces typically list fonts in a long, alphabetically-sorted menu that can be challenging and frustrating to explore. We instead propose three interfaces for font selection. First, we organize fonts using high-level descriptive attributes, such as "dramatic" or "legible." Second, we organize fonts in a tree-based hierarchical menu based on perceptual similarity. Third, we display fonts that are most similar to a user's currently-selected font. These tools are complementary; a user may search for "graceful" fonts, select a reasonable one, and then refine the results from a list of fonts similar to the selection. To enable these tools, we use crowdsourcing to gather font attribute data, and then train models to predict attribute values for new fonts. We use attributes to help learn a font similarity metric using crowdsourced comparisons. We evaluate the interfaces against a conventional list interface and find that our interfaces are preferred to the baseline. Our interfaces also produce better results in two real-world tasks: finding the nearest match to a target font, and font selection for graphic designs.


Peter O'Donovan, Jānis Lībeks, Aseem Agarwala, Aaron Hertzmann. Exploratory Font Selection Using Crowdsourced Attributes ACM Transactions on Graphics, 2014, 33, 4, (Proc. SIGGRAPH). BibTex

Supplemental Materials

Further Experiments and Study Details: supplemental.pdf

Google WebFonts used in our dataset: gwfonts.zip
These fonts were downloaded from https://www.google.com/fonts

Attribute Dataset: attribute.zip

Similarity Dataset & Matlab code for metric learning: similarity.zip


Thanks to John Hancock for his technical support. This research is supported in part by Adobe, NSERC,and CIFAR.