Document understanding is the use of AI to parse, interpret, and extract structured information from documents with complex layouts, including PDFs, scanned images, forms, invoices, and reports. It goes beyond simple OCR by understanding spatial relationships, tables, headers, and the logical structure of document content, producing machine-readable data from visual documents.
Document understanding combines computer vision and NLP to process documents. First, layout analysis identifies regions (text blocks, tables, figures, headers). Then OCR extracts text from each region. Finally, a document understanding model (like LayoutLM or Donut) uses both the text content and its spatial position on the page to classify regions, extract key-value pairs, and understand table structures.
Modern document understanding uses transformer models trained on both text tokens and their 2D bounding box coordinates. Models like LayoutLMv3 and Donut can perform document classification, entity extraction, table recognition, and question answering over documents. For PDFs, the pipeline includes PDF rendering, layout analysis (detecting text columns, tables, figures), and multi-page reasoning.
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