1. Identify the document types
Begin by separating digitally generated PDFs from scanned images. A digital PDF may contain selectable text, while a scan generally needs optical character recognition, or OCR, to identify characters in an image. Some files combine both. Extracting text that already exists can be simpler than applying image recognition to every page.
Describe layout variation, handwriting, tables and page quality. A consistent application form differs from a collection of supplier invoices with changing labels and arrangements. Include skewed scans, missing pages and attachments in the scope discussion. Document processing should recognise unsupported files and damaged inputs rather than silently returning an incomplete record.
2. Define fields and source evidence
Create a field specification before choosing a model. State each field’s name, type, whether it is required and how an absent value should be represented. For a UK invoice workflow, fields might include supplier name, invoice reference, issue date, currency, line items and totals. These are extraction targets, not a claim that every document contains them.
Keep the original text and its page location where practical. Normalisation changes the representation of a value, such as turning a written date into an agreed database format. Record that transformation without discarding the original. A date written only with digits may be ambiguous when the document’s country or convention is unclear; ambiguity should be reviewed rather than guessed.
3. Compare extraction approaches
Azure AI Document Intelligence and Amazon Textract provide document-analysis services. Compare them against your document types, required field structure, supported processing regions and integration needs. Examine provider documentation and contractual terms before sending personal or commercially sensitive records to a hosted service.
Tesseract is an OCR engine that can form part of a local processing pipeline. Local deployment gives direct infrastructure control but still requires preprocessing, layout handling and validation around the extracted text. A language model may interpret varied labels after OCR, yet it can also invent a missing value. Keep deterministic checks outside the model.
4. Validate meaning, not just format
A field can be formatted correctly and still be wrong. An invoice reference might accidentally be extracted from a purchase-order label. Compare required values with available source evidence and check relevant relationships. Where totals are involved, use ordinary arithmetic to check their relationship to line items, adjustments and tax amounts; do not ask a language model to perform the final calculation.
Use confidence values cautiously. A provider’s confidence score is not a universal probability of correctness and is not necessarily comparable with another provider’s score. Set review rules using observed results on appropriate documents and the consequences of a mistake. Required fields, contradictory totals and unfamiliar layouts can justify review regardless of a high confidence value.
5. Design a review queue
Give reviewers the extracted fields beside the relevant page or cropped source area. Let them correct values, mark an unreadable field and reject the document when necessary. Retain an audit record of changes with suitable access restrictions. Reviewing a generated summary alone makes it harder to see whether the extraction accurately reflects the document.
Separate duplicate detection from extraction. A repeated upload can be identified through file fingerprints or business references, depending on the task, but either method has limitations. A changed scan may represent the same invoice; identical files may arrive for different legitimate reasons. Define how duplicates are investigated before allowing automatic downstream processing.
6. Evaluate and connect carefully
Assess field-level accuracy, omissions, false additions and the time reviewers need to resolve exceptions. Include examples from the document types in scope and retain a separate evaluation collection when improving the pipeline. Overall document success can hide a critical error in a single field, so distinguish high-consequence values from cosmetic formatting.
A scoped service can connect ingestion, extraction, validation and an approved export into a business application. Agree retention for original documents, extracted records and processing logs separately. Remove unnecessary personal data and restrict access throughout. Begin with reviewed exports before permitting automatic record updates, and keep a clear route for correcting a result after it has entered the destination system.