Receipt extraction. From any photo.

Why receipts are hard

Receipts are the worst-quality input in the document-AI pipeline. Thermal paper fades within months. Smartphone photos arrive crumpled, rotated, or with a finger in the frame. Restaurant receipts split lines across tax, tip, and total in unpredictable orders. International receipts add currencies, languages, and tax regimes most parsers don't handle. OCR-only systems sit at 75–80% field accuracy on receipts and collapse on damaged images — which means employees spend their post-trip evenings re-typing data into expense forms.

How Cogneris does it

Cogneris's classifier identifies the receipt type (retail, restaurant, taxi, hotel folio, fuel, parking, freight) and routes to a specialist extractor. Image preprocessing handles glare, rotation, perspective skew, and partial folds. The extractor parses merchant identity, line items, tax breakdown by rate, payment method, and timestamps with per-field confidence. The validator runs arithmetic checks (line items + tax + tip = total) and flags receipts that don't reconcile before they hit your expense system.

Sample extraction output

What you get out of the box

Receipt fields and JSON shape

Receipt OCR API comparisons usually focus on field coverage and mobile-photo resilience. Cogneris returns merchant details, transaction date/time, line items, tax breakdown, tip, total, payment method, duplicate status, policy flags, confidence, and citations.

{
  "document_type": "receipt",
  "fields": {
    "merchant_name": { "value": "Trattoria del Borgo", "confidence": 0.98 },
    "transaction_date": { "value": "2026-04-18T20:42:00", "confidence": 0.96 },
    "total": { "value": 100.00, "currency": "EUR", "confidence": 0.99 },
    "payment_last4": { "value": "4421", "confidence": 0.94 }
  },
  "line_items": [{ "description": "Dinner", "amount": 84.00 }],
  "policy_flags": []
}

SDK links for developers

Use the Node.js SDK, Python SDK, or REST API reference to push receipt data into expense apps, card transaction matching, or a custom reimbursement portal.

Integration patterns

Cogneris pushes structured receipt data into the expense systems T&E teams already run. SAP Concur — direct expense-line creation with attachments. Brex, Ramp, Navan — receipt match against the card transaction with policy-flag passthrough. Expensify, Spendesk — native connectors with field-mapping templates. NetSuite Expense Reports — for organizations running T&E inside the ERP. Custom integrations — drop normalized JSON into the REST API or use webhooks for event-driven flows.

Compliance & trust

Receipts contain payment-card data and personal expense patterns. Cogneris masks card numbers in audit metadata by default (only the last 4 digits are returned), retains documents encrypted at rest with per-tenant keys, and offers configurable retention from 0 to 7 years to meet local tax-record obligations. See our trust page for the full posture: encryption, tenant isolation, sub-processors, GDPR DPA, CCPA, SOC 2 Type II in progress, and HIPAA BAA on Enterprise.

Get started

Pay-per-page pricing means you can start an evaluation today without an annual commit. Most teams ship their first receipt extraction into production within a week and reach steady-state accuracy on their employee-receipt mix in under 30 days.

Related extractors

Cogneris extracts dozens of structured document types. The closest neighbors to receipt extraction:

• Invoice extraction — vendor, line items, GL-code hints, and totals for AP automation.
• Bank statement extraction — transaction-level parsing used to reconcile expense reports against card and account statements.
• Payroll extraction — pay stubs and W-2s often grouped with receipts in employee reimbursement and onboarding workflows.

For broader context, see the IDP buyer's guide, the 2026 State of Document AI report, or estimate ROI at your volume.