Intelligent Document Processing
It uses AI and natural-language processing to read, classify, and extract data from documents by understanding meaning, not just characters. It handles the unstructured 80–90% of business data, cutting processing time 50–70% and errors 52–95%, with low-confidence cases routed to a human.
Beyond OCR
Plain OCR converts an image to text; IDP understands what the text means. That is the difference between reading characters and knowing which number is the invoice total versus the tax.
The human-in-the-loop advantage
IDP does not aim for 100% automation on day one. It routes low-confidence extractions to a human, whose corrections retrain the model, so accuracy climbs over time instead of plateauing.
The best IDP implementations treat human review as a feature, not a failure. Every low-confidence document a person corrects becomes training data, creating a continuous-improvement loop. This is why accuracy that starts around 90% climbs toward 99% within months, and why IDP handles the messy long tail of documents that broke earlier automation attempts.
Frequently asked questions.
What is the difference between OCR and IDP?
OCR converts an image into machine-readable text. IDP adds AI and natural-language processing to understand the meaning of that text, classifying the document and extracting the right fields even when the layout varies. IDP handles unstructured documents OCR alone cannot.