  Machine translation is one of the applications of natural language processing
which has been explored in different languages. Recently researchers started
paying attention towards machine translation for resource-poor languages and
closely related languages. A widespread and underlying problem for these
machine translation systems is the variation in orthographic conventions which
causes many issues to traditional approaches. Two languages written in two
different orthographies are not easily comparable, but orthographic information
can also be used to improve the machine translation system. This article offers
a survey of research regarding orthography's influence on machine translation
of under-resourced languages. It introduces under-resourced languages in terms
of machine translation and how orthographic information can be utilised to
improve machine translation. We describe previous work in this area, discussing
what underlying assumptions were made, and showing how orthographic knowledge
improves the performance of machine translation of under-resourced languages.
We discuss different types of machine translation and demonstrate a recent
trend that seeks to link orthographic information with well-established machine
translation methods. Considerable attention is given to current efforts of
cognates information at different levels of machine translation and the lessons
that can be drawn from this. Additionally, multilingual neural machine
translation of closely related languages is given a particular focus in this
survey. This article ends with a discussion of the way forward in machine
translation with orthographic information, focusing on multilingual settings
and bilingual lexicon induction.
