Authors - Unnati Parmar, Jatin Modh Abstract - Due to the high degree of infusion and morphology, Gujarati is regarded as a low-resource language in the Natural Language Processing field. The key reason why Gujarati can be classified as such is the lack of computing tools and annotated digitized corpus. Every single dialect of the language has its own morphological, lexical, and orthographic peculiarities since the language is extremely diverse. It includes the most diversified dialect – Kutchi – alongside Kathiawadi, Surti, Charotari, and Pattani dialects. The current paper focuses on the evolution in the sphere of Gujarati dialect identification via texts from 2021 till 2025. Morpheme segmentation, parts of speech identification, regional idioms identification, and neural machine translation model adaptation will be analyzed throughout this paper. This study looks at the transition from traditional grammar-based systems to modern deep learning algorithms. Performance metrics from the latest literature are used to identify research gaps. They include excellent results in the detection of idioms and high F1-scores for morphological tagging. Performance metrics of various models, such as transformers and Bidirectional Long Short-Term Memory network, are compared with DFA techniques. A framework for hybrid language models, combining both linguistics and neural networks, is proposed in the conclusion section of this literature review. Neural network models have been found to offer significant improvements in morphology when compared to traditional methods. In this paper, we address an inadequacy in the processing of informal language through the identification of disparities in resources between geographic variations. We propose a combination model that maintains geographic identity in modern-day computerized environments.
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