  The wide accessibility of social media has provided linguistically
under-represented communities with an extraordinary opportunity to create
content in their native languages. This, however, comes with certain challenges
in script normalization, particularly where the speakers of a language in a
bilingual community rely on another script or orthography to write their native
language. This paper addresses the problem of script normalization for several
such languages that are mainly written in a Perso-Arabic script. Using
synthetic data with various levels of noise and a transformer-based model, we
demonstrate that the problem can be effectively remediated. We conduct a
small-scale evaluation of real data as well. Our experiments indicate that
script normalization is also beneficial to improve the performance of
downstream tasks such as machine translation and language identification.
