Manual document extraction still breaks in places where it should work. Tables shift, fields move, and layouts change across vendors, formats, and scans. Traditional OCR reads text but misses structure, which leads to incorrect data mapping, broken workflows, and repeated manual checks. This becomes more visible in invoices, bank statements, and contracts where layout defines meaning. Layout-aware AI addresses this gap by reading both text and structure together. It identifies relationships between elements, not just characters on a page. In this post, we break down how layout-aware AI improves extraction accuracy, the technologies behind it, how it compares with older approaches, and where it delivers better outcomes at scale. What Is Layout-Aware AI in Document Processing? Layout-aware AI refers to models that understand both the content and the structure of a document.…