Learning to Kern -- Set-wise Estimation of Optimal Letter Space
Kei Nakatsuru Seiichi Uchida
Kerning is the task of setting appropriate horizontal spaces for all possible
letter pairs of a certain font. One of the difficulties of kerning is that the
appropriate space differs for each letter pair. Therefore, for a total of 52
capital and small letters, we need to adjust $52 \times 52 = 2704$ different
spaces. Another difficulty is that there is neither a general procedure nor
criterion for automatic kerning; therefore, kerning is still done manually or
with heuristics. In this paper, we tackle kerning by proposing two
machine-learning models, called pairwise and set-wise models. The former is a
simple deep neural network that estimates the letter space for two given letter
images. In contrast, the latter is a Transformer-based model and estimates the
letter spaces for three or more given letter images. For example, the set-wise
model simultaneously estimates 2704 spaces for 52 letter images for a certain
font. Among the two models, the set-wise model is not only more efficient but
also more accurate because its internal self-attention mechanism allows for
more consistent kerning for all letters. Experimental results on about 2500
Google fonts and their quantitative and qualitative analyses show that the
set-wise model has an average estimation error of only about 5.3 pixels when
the average letter space of all fonts and letter pairs is about 115 pixels.