Compare document intelligence APIs at a glance
Choosing a document AI service means balancing extraction accuracy, the formats and languages it supports, how many prebuilt models ship out of the box, and price per page. This table puts the major intelligent document processing APIs side by side — AWS Textract, Azure Document Intelligence, Google Document AI, and more — so you can pick the right engine for invoices, receipts, IDs, contracts, or general OCR.
How to read the table
- Accuracy is a 1–5 rating of structured extraction quality on clean documents.
- Formats is which file types are supported — PDF, images, Office, and more.
- Languages is the approximate count of supported languages for OCR.
- Prebuilt models flags ready-made extractors for invoices, receipts, IDs, and forms versus general OCR only.
- $/page is a list-price estimate; prebuilt and custom models cost more than raw text extraction.
Filter by provider and budget, search by service name, and click a column header to sort.
Plain OCR versus intelligent document processing
Plain OCR reads characters from an image and returns a text string. It is fast, cheap, and accurate for printed Latin text on clean scans. What it does not do: tell you which text is the invoice total versus the line-item price, identify table cells versus flowing paragraphs, or handle forms where key and value appear in separate visual zones.
Intelligent document processing (IDP) applies layout analysis and field
extraction on top of OCR. It returns structured data — the invoice_total,
vendor_name, and line_items as separate typed fields — which your application
can use directly without brittle string parsing. Prebuilt models for invoices,
receipts, and IDs handle common formats without any training data. Custom models
let you train on your own forms from a small set of labelled examples.
The cost difference is real: raw OCR is typically priced at a fraction of a cent per page; full structured extraction with a custom model can be ten to twenty times higher. For high-volume commodity OCR (digitising archived text, for example), raw OCR is the right choice. For anything where the extracted fields drive a downstream workflow — accounts payable, onboarding, claim processing — IDP pays for itself in reduced manual keying.
Typical use-case mapping
| Use case | Recommended approach |
|---|---|
| Invoice and PO processing | Prebuilt invoice/PO model (AWS, Azure, Google) |
| Receipt capture | Prebuilt receipt model or general OCR + parsing |
| Identity document verification | Prebuilt ID model (check jurisdiction support) |
| Proprietary form layouts | Custom model trained on your labelled samples |
| High-volume text digitisation | Raw OCR with language-model cleanup |
| Regulated or sensitive data | On-premise container or private-endpoint option |
Tips for picking a service
- For invoices, receipts, and IDs, prioritise services with mature prebuilt models — Azure, Google, and AWS all qualify.
- For proprietary form layouts, choose a service that supports training custom extraction models from a handful of labelled samples.
- For high volume, model your monthly page count against per-page tiers; raw OCR is far cheaper than full structured extraction.
- For on-prem or regulated data, check whether the service offers a container or private-endpoint deployment so documents never leave your network.
- Always test on your actual documents before committing. Accuracy on vendor benchmark datasets rarely matches accuracy on your own scans, especially for low-quality scans, handwriting, or non-Latin scripts.