Document AI: From OCR to Intelligent Agentic Extraction

Unlocking valuable data from scanned forms, multi-page reports, and complex layouts remains one of the biggest challenges in modern software. Standard text recognition often falls short when dealing with nested tables, visual charts, and unstructured layouts. This course guides you through the transition from basic text recognition to building advanced, agentic document extraction systems. You will learn how to read and process visual document components, ground your AI's responses in actual page layouts, and orchestrate intelligent agents to automate complex data workflows. What you'll learn: - Understand the foundational concepts of Document AI, OCR, and multimodal document processing. - Parse complex layouts, including tables, charts, and forms, to maintain structural integrity. - Implement agentic workflows that intelligently route, chunk, and extract information. - Apply modern validation techniques to ensure extracted data conforms to strict JSON schemas. - Configure layout-aware chunking strategies to improve retrieval-augmented generation pipelines. We begin with the core terminology of document parsing before moving into hands-on code examples that demonstrate how to connect OCR engines with modern language models. You will progress through written tutorials detailing schema-first extraction, multi-page document routing, and agentic validation. This course is designed for software developers, data practitioners, and tech-savvy beginners eager to master modern document processing. No prior experience with Document AI is required, though a basic familiarity with Python is helpful. Start reading today to transform unstructured documents into actionable structured data.