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

Towards Data Science reports that noisy text in retrieval-augmented generation (RAG) systems stems from three sources—user typos, fast-typing transcription noise, and OCR character errors—and that classical spell-check addresses only one of these, leaving embeddings to handle the rest. The article, part of the Enterprise Document Intelligence series (Vol.1 #B1), highlights the gap in current spell-checking approaches for RAG pipelines.

Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves Enterprise Document Intelligence Vol.1 B1 - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appear Enterprise Document Intelligence Vol.1 B1 - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appeared first on Towards Data Science. Key Takeaways - •Enterprise Document Intelligence Vol.1 B1 - Three sources of one problem - •This story was reported by Towards Data Science , covering developments in the newsletter space. - •AI advancements continue to reshape industries — read the full article on Towards Data Science for complete coverage. 📖 Continue reading the full article: Read Full Article on Towards Data Science → https://towardsdatascience.com/noisy-text-in-rag-typos-ocr-and-the-gap-classical-spell-check-leaves/