Mindee OCR API

Name: Mindee OCR API API
Brand: Mindee OCR API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLVisionbearer12 EndpointsREST

For Agents

Submit a document image or PDF and receive structured field extractions for invoices, receipts, IDs, and custom forms. Supports synchronous predictions for fast paths and asynchronous queues for batch processing.

Quickstart

Get started with Mindee OCR API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"extract data from an invoice or receipt"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Mindee OCR API API.

Submit invoices and receipts to /v1/products/{account}/{product}/{version}/predict and receive parsed line items, totals, vendor, and tax fields

Queue large document batches via predict_async and poll job status until extraction completes

Execute multi-step document workflows with /v1/workflows/{workflow_id}/executions to chain classification, extraction, and validation

Send corrected field values back through PUT /v1/documents/{document_id}/feedback to improve future model accuracy

GET STARTED

Start building with Mindee OCR API API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Mindee OCR API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Mindee OCR API, keeping it validated and agent-ready. Mindee is a document processing and OCR platform that extracts structured fields from invoices, receipts, identity documents, passports, and custom forms. The API exposes synchronous and asynchronous prediction endpoints, queue polling for long-running jobs, document feedback for retraining, and workflow execution for multi-step document pipelines. Predictions return parsed key-value pairs with confidence scores so agents can route documents without manual review.

Use Cases

Patterns agents use Mindee OCR API API for, with concrete tasks.

★ Accounts Payable Invoice Capture

Automate invoice intake by submitting supplier PDFs to the Mindee invoice product and receiving structured supplier name, invoice number, line items, tax, and totals. Agents can post extracted fields directly into accounting systems without manual keying. Synchronous predictions return in seconds for low-volume flows; asynchronous queues handle thousands of invoices per hour.

Submit invoice.pdf to /v1/products/mindee/invoices/v4/predict and return supplier_name, invoice_number, total_amount, and line_items as JSON

Receipt Expense Reporting

Extract merchant, date, total, and tax from receipt photos for expense management apps. The receipt product handles thermal prints, photos, and PDFs in over 60 languages, returning parsed fields with confidence scores so agents can flag low-confidence values for human review.

Send receipt.jpg to the receipt product predict endpoint and create an expense entry with merchant, date, and total_amount fields

ID and Passport Verification

Capture name, document number, date of birth, and expiry from passports and national IDs as part of KYC onboarding. Mindee parses MRZ codes and visual zones in a single call, returning structured fields plus crop coordinates so agents can build verification flows without bespoke ML.

Submit passport.jpg to the passport product and verify that the parsed expiry_date is in the future and the surname matches the user record

Agent-Driven Document Workflows

AI agents discover the Mindee API through Jentic by intent (parse a receipt, extract invoice fields). Jentic returns the predict endpoint with its input schema, the agent uploads the document, and parsed fields stream back. The Mindee bearer token stays in the Jentic vault so the agent never sees the raw secret.

Search Jentic for 'extract invoice data', load the Mindee predict schema, and execute against three queued invoice PDFs

Key Endpoints

12 endpoints — jentic publishes the only available openapi specification for mindee ocr api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/v1/products/{account}/{product}/{version}/predict

Synchronous document prediction

POST

/v1/products/{account}/{product}/{version}/predict_async

Queue an asynchronous prediction

GET

/v1/products/{account}/{product}/{version}/documents/queue/{job_id}

Poll async job status

GET

/v1/products/{account}/{product}/{version}/documents/{document_id}

Retrieve a parsed document

PUT

/v1/documents/{document_id}/feedback

Send field-level correction feedback

POST

/v1/workflows/{workflow_id}/executions

Execute a document workflow

POST

/v1/products/{account}/{product}/{version}/predict

Synchronous document prediction

POST

/v1/products/{account}/{product}/{version}/predict_async

Queue an asynchronous prediction

GET

/v1/products/{account}/{product}/{version}/documents/queue/{job_id}

Poll async job status

GET

/v1/products/{account}/{product}/{version}/documents/{document_id}

Retrieve a parsed document

PUT

/v1/documents/{document_id}/feedback

Send field-level correction feedback

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

The Mindee bearer token is stored encrypted in the Jentic vault. Agents call the predict endpoints through scoped Jentic execution requests; the raw token never enters the agent's context window or logs.

Intent-based discovery

Agents search Jentic by intent (e.g. 'extract invoice data', 'parse a receipt') and Jentic returns the matching Mindee predict operation with its input schema, so the agent calls the right product slug without browsing Mindee documentation.

Time to first call

Direct Mindee integration: 1-2 days to wire bearer auth, choose the right product version, and handle async queue polling. Through Jentic: under 30 minutes — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Rossum

Rossum focuses on transactional document AI for invoices and purchase orders with human-in-the-loop review built in

Choose Rossum when the workflow requires built-in approval queues and validation UI rather than raw field extraction

Alternative

ABBYY

ABBYY offers enterprise OCR and intelligent document processing across many document classes

Choose ABBYY for on-premise deployments or when broader document classification beyond invoices and receipts is required

Complementary

Airtable

Airtable stores parsed Mindee fields as structured records for downstream review and reporting

Use Airtable alongside Mindee to land extracted invoice or receipt fields in a queryable table for the rest of the team

FAQs

Specific to using Mindee OCR API API through Jentic.

Why is there no official OpenAPI spec for Mindee OCR API?

Mindee does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Mindee OCR API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Mindee OCR API use?

Mindee OCR API uses HTTP bearer token authentication. The token is sent in the Authorization header as `Bearer <token>`. When called through Jentic, the bearer token is stored in the Jentic vault and injected at execution time so the agent never sees the raw secret.

Can I extract line items from invoices with the Mindee OCR API?

Yes. Submit the invoice file to POST /v1/products/{account}/{product}/{version}/predict using the invoices product slug and Mindee returns parsed line_items, supplier_name, invoice_number, total_amount, and tax fields with per-field confidence scores.

How do I handle large document batches without timing out?

Use POST /v1/products/{account}/{product}/{version}/predict_async to queue the document, then poll GET /v1/products/{account}/{product}/{version}/documents/queue/{job_id} until the job_id transitions to a completed state. The completed response contains the parsed prediction.

How do I improve extraction accuracy on documents Mindee gets wrong?

Send corrected field values to PUT /v1/documents/{document_id}/feedback. Mindee uses these corrections to retrain the underlying models for custom products and surface the corrected values on subsequent predictions of the same document.

How do I extract receipt data through Jentic?

Run `pip install jentic`, then search Jentic for 'parse a receipt'. Jentic returns the Mindee predict operation with its input schema. Load the schema, supply the receipt file as the document field, and execute. Parsed merchant, date, and total fields return as JSON.