India is a country having multiple languages. The states in the country are based on languages; the people speak in those regions. Even in the same state, the language changes over short distances. Indian language has multiple kinds of literature that are difficult for another person in different regions to understand their language. This can be a useful tool for filling the gap between two languages with the help of NLP. As we know, NLP is a part of AI, which contains computer science and sentiment or linguistics. So, we can say that NLP is a technique which works as a bridge between humans and computer to fill the gap in computer language. It requires deep knowledge of statistics, computer language, and linguistics. So, it can be placed in the multidisciplinary area. Although research is going on in this field, still the solutions produced do not provide satisfactory results. It is due to the diversity of Indian languages and other challenges like unavailability of Natural Language Processing tools, unavailability of annotated corpora, absence of standards, ambiguity in conversion, an unmatched word in target languages, etc. Some Indian languages are easy to convert, e.g., from Hindi to Punjabi and vice versa, but some languages are very difficult to convert, e.g., from Urdu to Hindi or Punjabi. This paper discusses the challenges being faced by NLP researchers for Indian Language Conversions.

Keywords: NLP; languages; AI