Mapping the Typographic Latent Space of Digits

Alayt Issak, Sarthak Kakkar, Sair Goetz, Nik Bear Brown, Casper Harteveld

Keywords: Latent Space, Learning Representation, Beta-VAE, Typography

Abstract: Since the advancement of handwritten text to typefaces on a computer, the human mind has evolved towards corresponding various typefaces as norms of comprehension. Current-day typefaces, much like those written by hand, exist in disparities and are governed by consensus reached among Typographers. Currently, the PANOSE system, developed in 1998, is the most widely used and accepted method for classifying typefaces based on 10 visual attributes. In this work, we employ Disentangled Beta-VAE's, in an unsupervised learning approach, to map the latent feature space with a dataset of MNIST Style Typographic Images (TMNIST-Digit) of 0-9 digits across 2990 unique font styles. We expose the learning representation across a variety of font styles to enable typographers to contemplate and identify new attributes to their classification system.