Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves

Noisy text from user typos, transcription errors, and OCR mistakes poses a significant challenge for Retrieval-Augmented Generation (RAG) systems. Classical spell-correction techniques, such as Levenshtein distance and SymSpell, are effective for correcting simple, isolated typos that result in non-existent words. However, these traditional methods fail when a typo creates another valid word, or when dealing with complex transcription and OCR noise that changes the literal text but not the underlying meaning. The article posits that modern embeddings are better suited to handle these ambiguous cases, as they operate on semantic similarity rather than exact string matching. This allows them to bridge the gap left by classical spell-checkers in enterprise document intelligence pipelines.