  Typography is a ubiquitous art form that affects our understanding,
perception, and trust in what we read. Thousands of different font-faces have
been created with enormous variations in the characters. In this paper, we
learn the style of a font by analyzing a small subset of only four letters.
From these four letters, we learn two tasks. The first is a discrimination
task: given the four letters and a new candidate letter, does the new letter
belong to the same font? Second, given the four basis letters, can we generate
all of the other letters with the same characteristics as those in the basis
set? We use deep neural networks to address both tasks, quantitatively and
qualitatively measure the results in a variety of novel manners, and present a
thorough investigation of the weaknesses and strengths of the approach.
