OCR vs IDP:what is the difference?
OCR reads characters from images; IDP delivers validated structured data. OCR is a component, IDP is the workflow around it.
Text out versus data out.
OCR (optical character recognition) turns an image of a document into machine-readable text. That is the whole job: layout, meaning, correctness and downstream use are out of scope. IDP (intelligent document processing) wraps OCR in a workflow that classifies the document, extracts specific fields, validates them and delivers structured records.
The practical test is the output. OCR gives you a text file that still needs a human or a program to interpret it. IDP gives you named fields (invoice number, gross total, IBAN) that have been checked, with the uncertain cases routed to review.
OCR: recognition
Characters from pixels, including handwriting recognition in modern engines. No interpretation.
IDP: interpretation
Which document is this, which values matter, are they consistent, where do they go.
Together, not either-or
Every IDP pipeline contains OCR as an early stage; the value is added after it.
Where MiruIQ sits.
MiruIQ is the IDP layer: it runs OCR internally (on your infrastructure if needed) and adds schema-driven extraction, validation, cross-document checks and human review on top, delivering data instead of text.
OCR vs IDP questions
What teams comparing OCR and IDP ask most.
When you only need documents to be searchable or archivable as text: full-text search over an archive, digitizing books, making scans accessible. As soon as specific values must land in specific fields of a business system, plain OCR stops being enough, because it does not know which characters matter.
Yes, always: OCR is an early stage of every IDP pipeline. In MiruIQ, page images pass through OCR, the recognized text and layout feed classification and schema-driven extraction, and validation runs on the extracted values. You configure the pipeline; the OCR stage is built in.
See it on your own documents.
The 14-day free trial covers 200 documents: enough to define your schemas, run real files and measure the results before anyone calls you.