  In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that
contains a synthetic seed image dataset generation procedure for languages with
different numeral systems using freely available open font file datasets. This
seed dataset of images is then augmented to create a purely synthetic training
dataset, which is in turn used to train a deep neural network and test on
held-out real world handwritten digits dataset spanning five Indic scripts,
Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy
of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN)
that generates realistic digit images in the five languages, and also
quantitatively by testing a CNN trained on the synthetic data on the real-world
datasets. This establishes not only an interesting nexus between the
font-datasets-world and transfer learning but also provides a recipe for
universal-digit classification in any script.
