Extract the fields.
Check their meaning.

1. Examine the documents you actually receive

Collect authorised examples across the formats your process must handle. Born-digital PDFs may contain selectable text, while scanned PDFs contain page images. Photographs can introduce skew, glare and cropping. Tables, stamps, handwritten notes and multi-page attachments create different extraction problems. File extension alone does not establish how readable a document will be.

Define the document types included in scope: invoices, supplier forms, delivery notes or correspondence, for example. Specify the fields needed downstream and whether a missing field is acceptable. Avoid collecting everything simply because a service can return it. The required output should follow the business process, including who is permitted to access the resulting records.

2. Distinguish reading from interpretation

Optical character recognition, or OCR, converts text in an image into machine-readable characters. Extraction identifies useful fields or structures within that content. Classification identifies a document type. Validation checks whether extracted values meet the application’s rules. These are separate operations even when a supplier presents them through a single API.

For a regular form, layout-aware extraction may be more predictable than asking a general language model to interpret the entire page. For varied correspondence, a model may help identify relevant passages, but its output still needs validation. Keep the source location for each field where possible. Reviewers should be able to see why a value was produced without searching the whole file.

3. Compare services on your document structure

Azure AI Document Intelligence provides document analysis and extraction capabilities within Azure. Amazon Textract supports extraction of text and document structures in AWS. Google Cloud Document AI organises document processing through processors. These services have different interfaces and model options; check their official documentation for supported inputs, regions and the specific functions required by your workflow.

If the organisation already operates in one cloud environment, identity, storage and monitoring integration may simplify the surrounding work. That is an operational consideration, not proof of better extraction. Compare the same authorised documents across shortlisted approaches. Look at missed fields, table alignment and the effort needed to correct results. Review data retention and supplier terms alongside technical performance.

4. Validate values before accepting records

Set field-specific checks. A date should parse in the expected format; an invoice reference should be present where required; arithmetic should reconcile where the document provides relevant totals. UK documents may use day-first dates, while imported documents can follow different conventions. Preserve the original text when interpretation is ambiguous rather than silently choosing one meaning.

A confidence score is a supplier’s estimate, not a guarantee that a value is correct. Determine review rules using the consequences of an error and the behaviour observed on your evaluation documents. Route ambiguous values to a person with the original page beside the extracted field. Record corrections and reasons so recurring issues can be distinguished from isolated unreadable inputs.

5. Preserve the link between source and outcome

Assign a document identifier and maintain its relationship to extracted data, review status and downstream actions. Detect duplicate uploads before creating new records. Treat files as untrusted input: restrict accepted types, apply appropriate malware checks and separate processing permissions from general user access. Avoid placing document contents in routine diagnostic logs.

A document processing service can be scoped around ingestion, extraction configuration, validation, a review queue and a destination-system integration. Agree the included document families and how unsupported material will be handled. Define deletion and retention rules for both originals and extracted records. A successful extraction is only part of completion; the destination system must acknowledge the accepted record.

Use the official documentation for Azure AI Document Intelligence, Amazon Textract and Google Cloud Document AI to assess implementation details. Do not include sensitive documents in an initial enquiry; describe their structure and purpose instead.