Invoice OCR & data capturing

Enhance the effectiveness of your organization’s financial and administrative processes with Optical Character Recognition and data extraction. Process invoices automatically and safely with Klippa. Powered by machine learning.

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What we can do with invoices

Our machine learning technology can automate many invoice-related business processes via APIs and SDKs.
Invoice scanning
Invoice format and quality conversions
Invoice to text, UBL, CSV, XSLX or JSON with OCR
Data capturing on invoices
Classifying invoices and invoice line items
(Cross)Validating invoice data
Invoice management
Workflow optimization

Invoice data extraction

An image speaks more than a thousand words. Below is an example of the three steps our OCR engine takes to automatically extract data from invoices.

Upload invoices to the API

The first step is providing a picture or a PDF file of an invoice to our API. Usually this is done from a mobile app, email, FTP or web application.

The document can be sent cropped or uncropped (with background). If it is sent uncropped, the Klippa API will automatically crop the document.

The Klippa invoice scanning SDK can also be used in mobile apps. 

Image to text using OCR

As soon as a picture or PDF is sent, it is converted to a TXT file. In this step all the text from the document is extracted, but it is not yet structured.

Getting JSON output from the API

The Klippa Parser takes the TXT gained from the OCR in step 2 and converts it into structured JSON using machine learning. The JSON is then returned as output from the API. From here the invoice can easily be processed into your database. Wether you are processing invoices for accounting or loyalty purposes, Klippa is here to help.

The image on the left is a simplified example of a JSON response.
Reduce cost
Spend less on invoice processing by using Klippa.
Improve speed
Process invoices automatically within seconds. Perfect for accounting, accounts payable automation, cashbacks and loyalty.
Reduce errors
Prevent manual data entry mistakes with automated data extraction on invoices.
Prevent fraud
Automatically recognize errors, duplicates and fraud.
Which invoice fields can be extracted?
The default data fields (checkmarks) and automated checks (locks) are listed below. These can be customized. Additional fields can be extracted upon request.
Document type
Image quality
Country of origin
Invoice language
Merchant name
Merchant address details
Merchant contact details
Merchant website
Client details
Payment method
Card number
Amount of change
Date of purchase
Due date
Date of delivery
Total amount
VAT amounts
VAT percentages
Line item descriptions, quantity, prices and category
Invoice number
Purchase order number
Chamber of commerce number
VAT number
And many more fields
Find duplicates via image hashing
Identify fraudulent documents with crosschecks
Let's talk over the phone!
We love to explain more about Klippa via a short phone call. If you want us to call you back, just choose a date and time that suits you best. If you leave the date and time empty we will call you within 30 minutes!
We are available for calls in English, Spanish, French, German and Dutch during office hours (CEST timezone). You can reach us at +31 50 2111631.

Frequently Asked Questions

What does invoice OCR cost?
The pricing structure for Klippa invoice capturing depends on the amount of fields and the document volume. Both pay per use and monthly licenses are available. Get in contact with our product specialists to get an exact quote.
What types of invoices are supported?
Klippa can extract data from parking receipts, fuel receipts, payment receipts, grocery store receipts and receipts from all other types of stores.
Are invoice line items extracted?
Yes, Klippa supports line item extraction from invoices. We have a special machine learning algorithm for line item extraction. For every line, the quantity, description, price per unit, total price and VAT values are extracted. Optionally SKUs, weight and size can also be extracted. 

After the line item extraction Klippa performs line item classification. Using an additional algorithm we classify over 20 categories for each line. For example ‘Food’, ‘Electronics’, ‘Alcoholic’ and many other options. Based on these categories the VAT deductibility can be determined, loyalty points can be distributed and general ledger accounts can be suggested. This is a perfect solution for accounts payable automation.
What use cases are supported?
Klippa tries to extract as much data as possible from invoices via OCR and machine learning. Our goal is to support as many document types as possible. 

Many of our clients ask for quality detection, validity verification, warranty insights, invoice analytics, spending insights, cashbacks, loyalty, VAT reclaim, 2-way matching, 3-way matching and accounting. 

RPA accounts payable automation is currently our most popular solution. We can help you automate up to 95% of your invoice processing using OCR and machine learning. 
What languages does Klippa support?
All European languages are supported. Our engine performs best on receipts in English, Dutch, Norwegian, Danish, Swedish, Finnish, Italian, Portuguese, Spanish, German, Hebrew and French. Other languages can be supported on request, since we can train our machine learning models.
Is Klippa invoice capturing GDPR compliant?
All the services that Klippa offers are fully GDPR compliant. We only use ISO-certified servers within the European Union for processing invoices. A data processor agreement is in place. We do not store any of your or your customers data after processing.
Is there documentation for the invoice OCR API?
Klippa was founded by developers, which is why we understand the value of a well-documented API. Our documentation is created using SWAGGER and can be found via this link.

 Schedule a free online demonstration

Get a clear view of how Klippa can help automate your invoice processing. A demo takes just 30 minutes.

Please feel free to ask all your questions.
 Get in touch by email, phone or chat!
+31 50 2111631