Large-Scale Visual Font Recognition

This paper addresses the large-scale visual font recognition (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 visual 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 containing 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 solution 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.

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Introduced in the Paper:

VFR-Wild VFR-2420 VFR-447

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Font Recognition VFR-2420 LFE (FS, template model size 2048) Top 1 Accuracy 72.5 # 1
Top 5 Accuracy 93.45 # 1
Top 10 Accuracy 96.87 # 1
Font Recognition VFR-447 LFE (FS, template model size 2048) Top 1 Accuracy 91.35 # 1
Top 5 Accuracy 98.80 # 1
Top 10 Accuracy 99.62 # 1
Font Recognition VFR-Wild LFE (FS, template model size 2048) Top 1 Accuracy 52.61 # 2
Top 5 Accuracy 58.4 # 2
Top 10 Accuracy 62.14 # 1


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