Paper Title
HINDI LANGUAGE PROCESSING: A SURVEY

Abstract
Hindi language processing is of paramount importance for various reasons, particularly in the context of our digital era. Hindi is spoken by millions of people in India and worldwide, making it one of the most widely used languages globally. Developing effective language processing tools for Hindi is essential for enhancing communication, improving access to information, and enabling meaningful engagement with digital platforms for Hindi speakers. In this paper, we have explored different preprocessing techniques such as noise removal, normalization, stopword removal, stemming, lemmatization, and tokenization specifically tailored for Hindi text. The subsequent section delves into name entity recognition and its various types. Section three reviews techniques available for text summarization including classification-based, cluster-based, neural network-based, optimization-based, and fuzzy-based approaches. Furthermore, we elaborate on text classification methods encompassing traditional machine learning, deep learning, transfer learning, and ensemble learning. In section seven, we explore various types of machine translation such as Statistical Machine Translation (SMT), Neural Machine Translation (NMT), phrase-based translation, rule-based translation, and hybrid translation systems.