Post an invoice PDF or image to one REST endpoint and get back structured JSON: vendor, invoice number, dates, line items, sales tax, and total. Bearer-token auth, no per-layout templates, and the same AI engine that powers the web app. Test it in the browser below, then wire the invoice parser into your product.
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Teams that need invoice data inside their own AP tool, ERP integration, or accounting product usually underestimate what it takes to read a real invoice reliably. The result is months of work to match what a dedicated invoice OCR API already does.
Open-source OCR like Tesseract hands back a wall of raw text and tops out around 62 to 78% field accuracy on invoices, because it has no document understanding and no table extraction. You still write the logic that decides which number is the total, which is tax, and which row is a line item.
There is no standard invoice format. A template or zone that works for one supplier fails on the next. Keeping field extraction accurate across thousands of vendor layouts is a full-time job your team probably should not own.
Pulling the header total is easy. Parsing a line-item table into description, quantity, unit price, and amount, across merged cells, multi-page invoices, and wrapped rows, is where homegrown extraction quietly breaks. Each fix is another sprint that is not shipping your product.
Many vendors hide per-call costs behind "contact sales," which makes it hard for a technical lead to forecast spend at volume. You want a clear endpoint, a clear JSON response, and a clear price.
The ReceiptOCR invoice OCR API wraps the same AI extraction engine behind the web app in a simple REST interface. You upload an invoice, trigger extraction, and receive validated JSON your code can use directly.
Every call returns structured JSON with named fields for vendor, invoice number, invoice and due dates, subtotal, sales tax, total, currency, and an array of line items. No screen-scraping, no parsing raw OCR text on your end.
Authenticate with a single Bearer token in the Authorization header. Generate a key, drop it into your request, and you are calling the invoice parser in minutes with standard HTTP.
The model reads any invoice layout from any vendor without per-supplier configuration. You do not build or maintain parsing rules, and accuracy holds as new vendors appear.
Get individual line items with descriptions, quantities, unit prices, and amounts in the response, not just the grand total. That detail is what makes the data usable for PO matching, coding, and reconciliation.
Post a PDF, JPG, or PNG to the upload endpoint. Native digital PDFs, scanned paper invoices, and emailed images all return the same structured response.
Documented rate limits and a clear request flow mean you can plan for production load instead of guessing. Scale invoice processing inside your app without standing up your own OCR stack.
A straightforward upload-then-retrieve flow you can integrate in an afternoon.
POST your invoice PDF or image to the upload endpoint with your Bearer token. The API accepts PDF, JPG, and PNG files and queues the document for extraction.
Tip: Use the /api/documents/types endpoint to confirm supported document types programmatically.
Call the extract endpoint for the document. The AI engine reads the vendor, invoice number, dates, line items, subtotal, sales tax, and total from whatever layout the supplier used.
Fetch the extraction result and receive clean JSON your application can store, validate, or push into QuickBooks, Xero, NetSuite, or your own database. No manual review step required.
Built for US developers and product teams who need invoice data extraction inside their own software, without owning an OCR pipeline.
Add invoice capture to an accounting, spend, or fintech product without building extraction from scratch.
Wire invoice OCR into Zapier, Make, or a custom backend so vendor invoices flow into your books automatically.
Feed supplier invoices straight into your AP or ERP system through a single API call, with line items ready for coding.
Turn inbound PDF invoices into structured records your platform can three-way match against POs and receipts.
An invoice OCR API is a web service that takes an invoice PDF or image and returns the extracted fields as structured data, usually JSON. Instead of running optical character recognition and writing your own parser, you send the file to an endpoint and get back named values for vendor, invoice number, dates, sales tax, total, and line items. The API handles the hard part, turning a document into accounting-ready fields, so your application just consumes the result.
Open-source OCR returns raw text, which means you still own the logic that decides which token is the total and which row is a line item. That parsing layer is where most homegrown projects stall, because invoice layouts vary by vendor and change without notice. Independent 2026 benchmarks put self-hosted Tesseract around 62 to 78% field accuracy on invoices, since it offers no table extraction or field labeling, while purpose-built extraction APIs reach roughly 94 to 97% on clean invoices and 82 to 92% on low-quality scans. A dedicated invoice data extraction API gives you a maintained model that reads any layout, so you ship the feature in days and never chase a vendor-specific format again. For the no-code version of the same engine, see our invoice OCR software.
Integration follows three calls. You POST the invoice to the upload endpoint with a Bearer token, trigger extraction on that document, then retrieve the structured JSON result. The response includes the vendor, invoice number, invoice and due dates, subtotal, itemized lines with quantities and unit prices, sales tax, and total. Full request and response examples in cURL and JSON live in the API documentation.
The header total is the easy field. The value is in the line-item table: description, quantity, unit price, and amount for every row, captured cleanly across merged cells, wrapped text, and multi-page invoices. Because extraction is model-based rather than template-based, you get the same JSON structure across vendors without writing conditional logic for each supplier. That structure is what makes downstream PO matching and general-ledger coding possible. The trade-offs between template OCR and AI extraction are covered in depth on our invoice OCR vs AI extraction comparison.
Once you have structured fields, the integration is yours to define. Store the JSON in your database, map it to a QuickBooks or Xero import, or push it into an AP workflow for approval and payment. Every invoice returns the same field structure, so your downstream mapping is written once and reused. If your team needs receipts as well as invoices, the same endpoints power our receipt OCR API, and a plain spreadsheet workflow runs through AI invoice data extraction in the browser.
An invoice OCR API is a web service that accepts an invoice PDF or image and returns the extracted data as structured JSON. It reads the vendor, invoice number, dates, line items, sales tax, and total for you, so your application consumes ready-to-use fields instead of running OCR and writing parsing logic yourself.
The API returns clean JSON with named fields for vendor, invoice number, invoice and due dates, subtotal, sales tax, total, and currency, plus an array of line items with descriptions, quantities, unit prices, and amounts. Your code can store or map that response directly without parsing raw text.
Authentication uses a Bearer token passed in the Authorization header of each request. You generate an API key, include it in standard HTTP calls to the upload and extract endpoints, and you are integrated. Full cURL examples are in the API documentation.
Purpose-built invoice extraction APIs reach roughly 94 to 97% field accuracy on clean invoices and 82 to 92% on low-quality scans, while self-hosted Tesseract falls to about 62 to 78% because it returns raw text with no table extraction or document understanding. The API uses the same AI engine as the web app and holds accuracy across vendor layouts.
Yes. The upload endpoint accepts PDF, JPG, and PNG files. The same extraction engine handles native digital PDFs, scanned paper invoices, and emailed images, returning the same structured JSON regardless of the input format.
Yes. The response includes an array of individual line items with descriptions, quantities, unit prices, and amounts, alongside the subtotal, sales tax, and grand total. That detail is what makes the output usable for PO matching, GL coding, and reconciliation in your app.
Yes. The API publishes documented rate limits so you can plan for production load. The request flow and current limits are described in the API documentation, which lets you size your integration before going live.
Yes. That is the primary use case. Product teams, automation engineers, and finance tech teams call the REST endpoints to add invoice capture to accounting platforms, AP pipelines, and ERP integrations without building or maintaining their own OCR stack.
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The same AI engine in a browser app for AP teams and bookkeepers.
See how the engine reads invoices field by field, no code required.
The receipt counterpart: the same endpoints return receipt JSON.
Process hundreds of invoices and receipts in a single batch.
