Intelligent document processing (IDP) is technology that automatically captures, classifies, extracts and validates data from documents such as invoices, forms, claims and contracts, using OCR, machine learning and language models, then sends the data to business systems. Low-confidence results go to people for review.
How is IDP different from OCR?
Optical character recognition (OCR) converts an image of text into machine-readable characters. It does not know what the text means. IDP builds on OCR: it identifies the document type, understands layout and context, finds the specific fields a process needs, checks them against rules and passes clean data on.
In practice, OCR answers "what characters are on this page?" while IDP answers "this is an invoice from supplier X for amount Y, due on date Z, and it matches purchase order N."
What are the steps in an IDP workflow?
A typical IDP workflow has six steps: ingest documents from scanners, email, portals or mobile capture; pre-process images to improve quality; classify each document by type; extract the required fields and tables; validate the results with business rules and confidence thresholds, routing exceptions to people; and export data and documents to ERP, CRM, ECM or RPA bots.
Which processes benefit most from IDP?
Processes with high document volumes and repetitive data entry benefit most: accounts payable invoices, purchase orders, insurance applications and claims, mortgage and loan documents, customer onboarding and KYC, logistics and customs documents, and HR forms.
How accurate is intelligent document processing?
Accuracy depends on document quality, variability and the platform, so it should be measured on your own documents. Well-designed solutions combine trained models, validation rules and human review so that the data entering business systems is accurate even when the model is uncertain.
How do you choose an IDP platform?
Compare platforms on the document types and languages you need, accuracy on a sample of your documents, handling of handwriting and tables, human-in-the-loop tools, integration options, deployment model, security and total cost. Leading options include Tungsten Automation, ABBYY, Hyland, OpenText, Microsoft Azure AI Document Intelligence and Amazon Textract.
How should an IDP project start?
Start with one high-volume document type, collect a representative sample, define the fields and validation rules, run a proof of value to measure accuracy and straight-through processing, then expand to further document types once the operating model works.
