solution for all your document needs

Our OCR engine extracts text from images and other formats, using AI.

Simple pricing

Need custom plan? Contact sales

Convert image to text

Our Advantages

Easy integration
Our detauled documentation allows you to easily integrate our API services into your workflow.
Low prices
We offer affordable prices for our API with discounts for startups.
Fast and scalable
We offer low latency API response and can set up a scalable service for custom projects.
Extensive Support
We offer quick response and support. If needed, we can provide you with custom services tailored to your requirements.

Use cases

Our APIs can be used in a wide range of use cases and fields.

Contact us to learn more

Automate insurance claims processing and achieve higher transactions throughput.


Use OCR API to process and extract documents, such as judgments, filings, statements and others.


Automate processing of invoices, vouchers and othe documents produced in the retail chain.


Over 600 clients have trusted the services of our company.

" I am really enjoying the platform and I think you are doing some really innovative stuff. "

" Thanks for excellent service and support. "

" I will absolutely endorse you guys and let people in my chat as well as my followers know how awesome this platform is! "

What is OCR, how does it work, what are its benefits and use cases?

OCR (Optical Character Recognition) is the technology that automates the conversion of any image/document containing printed or hand-written text into editable and searchable digital text.

An example is a collection of company's documents on particular topic that were printed out recently or some time ago.

Using OCR, we can turn all those document into digital form of rich text documents with full editability, enabling users to search for keywords across thousands of pages in just seconds!

OCR thus constitues a standard and important part of digitalization drive in most companies, because its benefits are often more visible when compared to some other AI/machine learning model initiatives where seeing the value added may require more expert knowledge in machine learning and data science.

Use cases of OCR

From brick and mortar retail to e-commerce, finance, transportation & logistics to healthcare and manufacturing - businesses increasingly rely on image capture technology to process their documents.

Text recognition (OCR) is one of the most important technologies for document scanning today. It is one of the foundations of digitalization processes in the companies and can produce a large amount of data that can then be analyzed through data science and machine learning projects.

Businesses in key industries are also increasingly making more use of smart devices in their day-to-day operations. For example, retail outlets often scan customer receipts to build customer profiles, while parcel couriers and delivery services need to scan parcels and label them with barcode tracking data.

Manufacturers also use OCR technology to develop quality control systems, manage inventory and improve production efficiency.

Some of the other use cases of OCR:
  • entry of business / legal documents, such as passport, invoices, statements
  • automatic processing of insurance claims
  • car number plate recognition
  • identification of traffic signs
  • business cards processing
  • bulk processing of business documents

Benefits of OCR

An OCR system enables you to centralize your data gathering processes, and manage it more efficiently. By utilizing this technology, you’ll be able to reduce costs associated with processing documents and accelerate delivery processes.

In fact, by automating your document routing and processing, an OCR system can increase employee productivity by ensuring they have the most up-to-date information when they need it.

Data capture software with OCR capabilities improves business processes by increasing accuracy and accelerating workflows. The system can reduce costs, eliminate manual input errors, automate processing of documents, centralize data and improve your customer service.

How does OCR work (technical)

OCR systems usually pre-process images in order to improve the likelihood of text recognition. Pre-processing techniques include (but are not limited to) the following:
  • deskewing - some documents may be skewed when collected, thus they may need to be tilted by some angle in order to make them horizonal or vertical.
  • despeckling - removing spots from the document and smoothing of the borders
  • binary filtering - conversion of an image to the black and white format.
  • removal of lines
  • identification of paragraphs and other specific blocks. This is especially relevant when the document contains tables.
  • identification of lines and words
  • character segmentation - isolation of characters in images
After pre-processing, the data is sent to OCR algorithm. Modern OCR algorithms use neural nets to recognize complete lines of text, instead of character by character recognition, which was more common in earlier OCR algorithms.

'; ?>