Spell checking systems have evolved significantly from simple dictionary-based approaches to advanced neural network models capable of context-aware error correction.This paper presents a comprehensive literature review of spell checking techniques used by different researchers, tracing their development from traditional rule-based and statistical methods to modern machine learning and deep learning approaches.In this paper traditional methods such as dictionary lookup, edit distance algorithms, n-gram models, and probabilistic techniques are addressed alongside recent advancements involving recurrent neural networks (RNNs), transformers, and large language models.Comparative analysis of existing studies reveals that neural spell checkers outperform conventional systems in contextual understanding and correction accuracy, although they require substantial computational resources and training data.The review also identifies current research gaps and emerging trends in intelligent spell correction systems.This study aims to provide researchers and practitioners with a clear understanding of the evolution, methodologies, and future directions of spell checking technologies in natural language processing.
Kaur et al. (Wed,) studied this question.