How Multimodal Document Parsing Works: From LayoutLM to Donut

How Multimodal Document Parsing Works: From LayoutLM to Donut Most AI systems are great at understanding clean text. But the real world doesn't send you clean text. It sends you PDFs, scanned invoices, handwritten forms, and multi-column research papers. This is the unstructured data problem, and it's one of the hardest open challenges in applied AI. This article breaks down how modern multimodal models tackle document understanding, specifically LayoutLM and Donut, and why this matters for the next generation of AI agents. Why Plain NLP Fails on Documents A standard language model reads text as a flat sequence of tokens. But documents are not flat. A table, an invoice, or a form has spatial structure — where something appears on the page is just as important as what it says. Consider a receipt. The word "Total" appears near the bottom right. The number next to it is the amount due. A model reading raw text has no idea about this spatial relationship. It just sees "Total" and "47.50" s