How do multimodal LLMs handle OCR / document understanding?

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Answer

$(1) Native tokenization: chop the image into patches (16-32 px), pass through a vision encoder.$
$Modern VLMs (Claude 3.5, GPT-4o, Qwen-VL) do surprisingly good OCR without external OCR pipelines.$
$(2) Dynamic resolution: process high-res regions (text-dense) at higher resolution, background at lower.$
$(3) OCR-augmented: run classical OCR (Tesseract, TrOCR, PaddleOCR) alongside the VLM and combine.$
$Best practice for production: VLM for general document QA; add OCR + layout parsing (Docling, LayoutLM) for structured docs.$

Check yourself — multiple choice

Can't do OCR
Native patchification + dynamic resolution; VLMs are strong at general OCR — combine with classical OCR / layout tools for structured docs
Only external OCR
Impossible

VLM OCR: native + dynamic resolution; add classical OCR / layout for structured docs.

#multimodal

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