What is Document Understanding
Document Understanding - AI-powered extraction of structured information from complex document layouts
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.
How It Works
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.
Technical Details
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.
Best Practices
- Preprocess documents to consistent DPI and orientation before analysis
- Use layout-aware models rather than plain OCR for structured documents
- Extract and preserve table structures as separate data objects
- Handle multi-page documents with cross-page context awareness
- Validate extracted data against expected schemas for quality assurance