Invoice OCR
Invoice OCR on ExactRead: compare models on the same invoice, catch extraction errors in totals and line items, then export vendor, terms, and table text as TXT or JSON.
Upload a document and compare models right here — no need to leave this page.
An invoice OCR error is not cosmetic — a missing decimal, a swapped vendor, or a dropped line item flows straight into your books. ExactRead is built for exactly this risk: it runs invoice OCR through more than one model on the same file and shows the outputs together, so a wrong total on one engine stands out against a correct one on another. Confidence and warning fields point reviewers at the fields most likely to be wrong.
Native OCR engines like AWS Textract and Azure Document Intelligence go beyond plain text: they target key-value pairs, tables, and form fields, which fits invoices, receipts, and structured documents. Mistral OCR reads dense invoice PDFs directly. Start with a native engine, then cross-check the tricky fields against a vision model such as GPT-5.4 in the same run.
Tell the workbench the document is an invoice before you run it. The invoice content type steers the extraction prompt toward totals, tax, payment terms, vendor details, and line-item tables, which improves how the structured JSON is organized for downstream use.
Accepted invoice OCR output downloads as plain TXT or as structured JSON that keeps full text, detected tables, document type, confidence, and warnings. That drops cleanly into a review queue, a bookkeeping tool, or internal automation. Review accepted output before it reaches accounting — OCR assists the check, it does not replace it.
Real invoices are messy: foreign templates, multi-currency totals, handwritten annotations, stamps, and two-column layouts all trip up a single engine. This is where invoice OCR benefits most from comparison — one model may recover a currency symbol or a nested tax line that another drops. Set the invoice content type, run a native OCR engine and a vision model together, and reconcile the totals across both outputs before you export.
Drop a invoice onto the workbench or click to browse. Supported inputs are PNG, JPEG, and WebP images, plus PDF for PDF-capable models.
After upload, the model list is filtered to the OCR engines that actually support your file, so you never start a job that cannot run.
Choose a single model when speed and cost matter, or compare mode to run several OCR models on the same invoice at once.
Each result keeps its model, status, confidence, and warnings, with the recommended output highlighted so you can judge accuracy quickly.
Accept the output that reads your invoice most faithfully, then copy it or export TXT or JSON for the next step.
Capability, format support, and credits come straight from the model catalog — a starting point, not a ranking. Test on your own documents to decide.
| Model | Provider | Type | Formats | Credits / doc |
|---|---|---|---|---|
| AWS Textract | AWS | Native OCR engine | Images and PDF | 2 |
| Azure Document Intelligence | Microsoft Azure | Native OCR engine | Images and PDF | 2 |
| Mistral OCR | Mistral AI | Native OCR engine | Images and PDF | 2 |
| GPT-5.4 | OpenAI | Vision-language model | Image files | 3 |
Supported formats
Invoice images and PDF-capable invoice files
Model guidance
Start with native OCR, then cross-check with a vision model
Credit note
Multi-model comparison is useful for totals, lines, and vendors
The OCR prompt asks models to preserve full text, tables, document type, confidence, and warnings, and the invoice content type steers toward totals, tax, and terms. Always review accepted output before using it for accounting.
Native OCR engines with forms and tables support — AWS Textract and Azure Document Intelligence — fit structured invoices, while Mistral OCR reads invoice PDFs directly. Cross-check unusual layouts with a vision model like GPT-5.4.
Yes. Accept a result and save it as an invoice preference with the invoice content type. Future invoice uploads start from that model, cutting repeat testing.
The structured JSON export keeps detected tables, so line items are preserved as structured data rather than flattened text. Complex or unusual tables should still be checked against the original invoice.
You run one invoice per job, but recent documents and saved preferences stay available so processing a batch is fast. Each accepted result exports independently as TXT or JSON.