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This article determines the current status of available datasets, MT and MTn systems, along with commenting on the validity of currently available evaluation metrics like BLEU for Indian languages. The review also discusses the scope for future research in this field. It explores different approaches like statistics oriented, example oriented, and neural network-oriented MT techniques implied in MT tasks, along with providing insight into the work carried out so far for Indian languages. The lack of readily available grammatical rules, the distinction between proper and common nouns, and large datasets, along with additional linguistic complexity compared to many other languages, makes developing such systems for Indian languages even more complicated. MT and MTn systems are an evolving field of computational linguistics and are considered to be incredibly challenging to develop. It also comments on the validity and viability of various evaluation metrics for Indian languages. This paper is unique in providing a detailed review of all steps involved in the NLP system development pipeline, from the creation and collection of data to the development of the system, and furthermore, the evaluation and analysis of the system. This paper presents a review of Natural Language Processing (NLP) techniques like Machine Translation (MT) and Machine Transliteration (MTn), along with providing an analytical study of evaluation metrics such as BLEU (BiLingual Evaluation Understudy) score and discussing datasets available for MT and MTn systems in Indian languages.

In today’s global scenario, frequent international and domestic interactions necessitate the application of Machine Transliteration and Translation systems to overcome the language barrier.

Machine translation vs transliteration