Azure Document Intelligence
Azure Document Intelligence is a Microsoft service for extracting text, layout and structured information from documents. Using it well involves choosing supported models, connecting extracted fields to page evidence and validating results against the application's requirements, especially when document layouts or image quality differ from the configuration examples.
What it is
The service provides document analysis capabilities including OCR and layout extraction, along with models for supported document types and custom extraction scenarios. An analysis response can expose text, tables, fields and locations depending on the selected model and API. These are machine-generated interpretations of a document, not automatically verified business records. The service is a particular implementation of Document AI, whereas OCR, layout analysis and information extraction are broader capabilities. Model selection, supported input formats and API version affect what information is returned and how it should be interpreted.
What the work involves
The practitioner checks the current model and API documentation, prepares representative documents and maps responses into an application schema. Validation handles missing values, normalization and consistency rules rather than blindly copying every field. Source coordinates or spans allow review of ambiguous extractions. Useful artifacts include the analysis adapter, field mapping, annotated test documents and a review policy. Evaluation covers unfamiliar suppliers, rotated scans and complex tables, while operational work handles authentication, asynchronous results, errors and controlled retention of sensitive source documents.
Illustrative example
An accounting application sends invoices to an appropriate extraction model and maps line items, currency and totals into proposed records. The application checks that line totals agree with the invoice total and that the supplier matches an approved account. An ambiguous amount is shown with its source page region for review. A new supplier's invoice layout enters the evaluation collection before automated processing is expanded, so apparent success on one familiar template does not determine the whole rollout.
Limits and common mistakes
Supported models and response structures evolve, and a confidence value is not a universal probability of correctness. Handwriting, unusual layouts or low-resolution scans can cause recognition and field-association errors. A service response can be technically complete while a critical field is wrong. Application acceptance therefore depends on evidence-based validation and real document coverage, with an explicit path for manual review when extraction cannot support the required record.
Prerequisites
Related skills
- → is an instance of: Document AI
Sources and further reading
- Azure Document Intelligence overview
Official service capabilities, model families and document analysis concepts.
Last updated: 2026-10-10