Spelling Correction

Spelling correction, aiming to automatically identify and rectify spelling errors in text, is a crucial area of natural language processing with applications ranging from improved user experience in search engines to enhanced accuracy in medical transcription. Current research emphasizes the use of deep learning models, particularly transformer-based architectures like BERT and variations thereof, often combined with techniques like Levenshtein distance or phonetic analysis to improve accuracy, especially for real-word errors and low-resource languages. These advancements are significant for improving the quality of digital text across various domains and languages, impacting fields from healthcare to education.