Learning Typographic Style
Abstract
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.
