Cognitive data capture mimics how a human mind reads structured documents using AI. This has two major points of impact: it learns to recognize information from examples rather than requiring expert configuration, and it can recognize a lot of information even in layouts of documents not seen by the system before.
Contrary to manual data entry or traditional OCR, cognitive data capture does not require a sizable workforce and the setup of endless rules or templates (detailed comparisons of effort are covered in our TCO analysis series).
Rossum’s cognitive data capture AI uses deep neural networks to recognize patterns in documents, and that is how the technology infers the underlying general structure of business documents like invoices in a similar way a human mind does. Moreover, Rossum’s unique neural network architecture ensures the high accuracy you may observe and allows Rossum to adapt to all kinds of layouts.
Refer to our founders’ blog series on cognitive data capture for an in-depth look at what is the technical difference between legacy OCR and cognitive data capture, and exactly how does Rossum’s technology work.