Document AI

Documents carry meaning through both content and arrangement. OCR recognizes text in an image; layout analysis identifies regions such as headings or tables; extraction maps evidence to fields or relationships. Vision-language models can also interpret pages, sometimes alongside specialist OCR and parsing tools. These components may be combined in a pipeline or provided by a service. Document AI is broader than OCR and distinct from retrieval: extraction interprets the document, while a retriever decides which document or region to inspect. Preserving structure helps downstream generation avoid losing associations between values and labels.

The practitioner identifies required fields and document families, creates annotated examples and evaluates extraction at both field and document level. Ingestion retains page coordinates or text spans so reviewers can inspect errors. Normalization and business validation follow recognition: an extracted amount still needs a currency and consistent total. Useful artifacts include the document schema, parsing pipeline, source locator and review workflow for uncertain cases. Tests cover scans, rotated pages, merged table cells and layouts that differ from the examples used to configure the extractor.

Recognition errors can propagate through normalization and look plausible in a generated summary. Tables, handwriting and unfamiliar layouts remain difficult, and confidence values are implementation-specific rather than universal correctness probabilities. A model can also infer a field that is absent from the page. Evaluation needs actual document variety and source-grounded checks. Processing sensitive documents requires controlled access and retention, regardless of whether extraction runs locally or through a managed service.