  This research introduces a state-of-the-art Persian spelling correction
system that seamlessly integrates deep learning techniques with phonetic
analysis, significantly enhancing the accuracy and efficiency of natural
language processing (NLP) for Persian. Utilizing a fine-tuned language
representation model, our methodology effectively combines deep contextual
analysis with phonetic insights, adeptly correcting both non-word and real-word
spelling errors. This strategy proves particularly effective in tackling the
unique complexities of Persian spelling, including its elaborate morphology and
the challenge of homophony. A thorough evaluation on a wide-ranging dataset
confirms our system's superior performance compared to existing methods, with
impressive F1-Scores of 0.890 for detecting real-word errors and 0.905 for
correcting them. Additionally, the system demonstrates a strong capability in
non-word error correction, achieving an F1-Score of 0.891. These results
illustrate the significant benefits of incorporating phonetic insights into
deep learning models for spelling correction. Our contributions not only
advance Persian language processing by providing a versatile solution for a
variety of NLP applications but also pave the way for future research in the
field, emphasizing the critical role of phonetic analysis in developing
effective spelling correction system.
