OCR (Optical Character Recognition)
Technology that extracts text from images, scanned documents, and PDFs — in 2026, OCR is often built into multimodal LLMs (Claude, GPT-4o, Gemini) rather than requiring a separate service.
OCR has been around for decades, but the 2024–2026 shift is profound: multimodal LLMs now do OCR as a side-effect of vision, with dramatically better accuracy on noisy or complex documents than traditional OCR engines. Send Claude or GPT-4o a photo of a receipt; you get clean structured text back.
Traditional OCR (Tesseract, Google Cloud Vision OCR, AWS Textract) is still better for high-volume document pipelines where cost-per-page matters more than reasoning. For ad-hoc document understanding, multimodal LLMs win on quality.
In 2026 the practical question is: am I extracting text (use traditional OCR or multimodal LLM) or understanding documents (use multimodal LLM with prompting). The latter is where the new value lives.
Frequently asked
Is OCR still relevant when LLMs can read images?+
Yes for high-volume document pipelines where per-page cost matters. Traditional OCR is 10–100× cheaper per page than multimodal LLMs. For low-volume document understanding, LLMs win on quality.
What is the most accurate OCR in 2026?+
For dense documents (forms, tables, receipts): multimodal LLMs (Claude Sonnet 4.6, GPT-4o, Gemini 2.5). For clean printed text at scale: Tesseract is free and good enough. For business-critical at scale: AWS Textract or Google Cloud Vision.