Scanning a document and understanding it are different jobs. Optical character recognition (OCR) turns text in an image or scanned file into machine-readable text. Intelligent document processing (IDP) is a broader label for a workflow that may also classify a document, extract named fields or tables, validate values, and route uncertain cases to a person. The term is used differently across products, so compare the steps a tool actually performs rather than relying on its label.
When OCR may be enough
OCR can be a fit when the main need is to make words searchable or copyable—for example, finding a delivery reference in a scanned letter or moving a short note into a record for later review. The output is text and, in many systems, its position on the page. Microsoft’s text-recognition documentation describes this as extracting printed or handwritten lines from images and documents.
Plain text still needs interpretation. OCR might read “RM 1,280” correctly, but that alone does not tell a system whether it is a subtotal, tax, balance, or amount due. If a staff member must identify and retype the fields each time, the process has not yet gained structured extraction.
When a wider document workflow helps
A broader approach may help when documents arrive in recurring business processes and the team needs information placed into defined fields. Imagine invoices from several suppliers: a workflow might identify the document type, find the supplier name, invoice number and total, check that required fields exist, then send the result to an accounting queue. Microsoft’s document-processing overview explains how a trained model can extract defined information and then be used in a Power Automate flow or Power Apps app. The example here is illustrative, not a claimed OmniMesh implementation.
Before choosing a tool, gather representative samples: different suppliers, older scans, rotated pages, tables, stamps, and the languages your team actually receives. Record which fields matter and what a correct answer looks like. Test the complete journey from upload to the destination record, not only a model preview. Check how the system handles duplicate files, missing pages, unreadable totals, and a temporary connection failure.
Design human review around risk
Do not treat a confidence score as proof that a field is correct. It is a signal to help decide what to check. Microsoft’s confidence-score guidance explains that field and table-cell scores can help prioritize outputs for human review. A practical workflow can route uncertain values, failed validation rules, or unfamiliar document types to a named reviewer. A person should also be able to compare the proposed value with the original page, correct it, and leave a reason when needed.
Set review rules according to the cost of an error. A misspelled non-critical reference may be easy to correct later; a wrong payment amount or customer identifier can have a wider effect. Begin with human checks for consequential fields and edge cases. Only reduce review after testing against real examples and measuring the mistakes that matter to the business. A vendor’s sample score is not a guarantee for your supplier mix, scans, or workflow.
Choose by the whole process
Use OCR when searchable text solves the problem. Consider document processing when you need repeatable field extraction and routing. In either case, plan where files are stored, who can access them, how long they are retained, and what happens when processing fails. Compare the time staff spend correcting results with the work the automation removes. Keep an accessible manual route while the process is being evaluated.
Learn more about OCR & Intelligent Document Processing.
The right first step may be to standardize the intake form, ask suppliers for clearer files, or automate only the handoff after a person has checked the values. If you are weighing OCR, extraction, or an integration for a document process, describe the documents and review needs to OmniMesh.