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This paper addresses the large-scale visual font recogni- tion (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although vi- sual font recognition has many practical applications, it has largely been neglected by the vision community. To address the VFR problem, we construct a large-scale dataset con- taining 2,420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision. As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable so- lution based on the nearest class mean classifier (NCM). The core algorithm is built on local feature embedding, lo- cal feature metric learning and max-margin template se- lection, which is naturally amenable to NCM and thus to such open-ended classification problems. The new algo- rithm can generalize to new classes and new data at lit- tle added cost. Extensive experiments demonstrate that our approach is very effective on our synthetic test images, and achieves promising results on real world test images.

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