  We introduce OmniPrint, a synthetic data generator of isolated printed
characters, geared toward machine learning research. It draws inspiration from
famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of
generating a wide variety of printed characters from various languages, fonts
and styles, with customized distortions. We include 935 fonts from 27 scripts
and many types of distortions. As a proof of concept, we show various use
cases, including an example of meta-learning dataset designed for the upcoming
MetaDL NeurIPS 2021 competition. OmniPrint is available at
https://github.com/SunHaozhe/OmniPrint.
