Past OCR: Building Document Extraction That Survives Tables and Forms
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New course: Document AI: From OCR to Agentic Doc Extraction, built with @LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing…
Context
The Document AI course teaches developers to extract information while retaining the page layout needed to interpret it. It uses LandingAI's Agentic Document Extraction to process documents visually, produce structured text, and return named fields in JSON, a format software can consume.
The useful connection is between an extracted value and its source. A developer specifies the fields to collect, and the system associates extracted information with bounding boxes marking locations on the page. That gives someone checking a result a route back to the relevant document region. The course presents this as a workflow for handling layouts such as tables and forms; its description supplies no measured accuracy improvement.
- RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
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