Abstract

This paper addresses the large-scale visual font recogni-tion (VFR) problem, which aims at automatic identificationof the typeface, weight, and slope of the text in an imageor photo without any knowledge of content. Although vi-sual font recognition has many practical applications, it haslargely been neglected by the vision community. To addressthe 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, local feature metric learning and max-margin template selection, which is naturally amenable to NCM and thus to such open-ended classification problems. The new algorithm can generalize to new classes and new data at little 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.