Optical Character Recognition


Why is this interesting?

Converting a printed text into a digital form has been around for quite some time now. In the past, we used different scanning tools, but those are expensive; now, we can leverage AI technologies.

Think of scanning the meter of your electricity, scanning your passport, scanning the annoyingly long WI-FI code at a friend’s house. OCR applications are mainly focused on increasing process efficiency as well as improving its user friendliness. These OCR algorithms can easily be embedded in your app, website or product.

Imagine having to extract information from tons of printed documents. Too often this has to be done manually. Therefore, use OCR instead to automatically retrieve all the necessary information in a digital manner.

  • Step 1: Copy the URL of the image that you want to analyze
  • Step 2: Paste the URL in the predefined box and sent the image to our algorithm.

To analyze the images and retrieve the textboxes, we used the open source programming language Python in combination with the Cognitive services API from Microsoft...

To analyze the images and retrieve the textboxes, we used the open source programming language Python in combination with the Cognitive services API from Microsoft. To create our web page, Flask is leveraged as this allows us to easily pass along information from the web page to our python scripts and vice versa. To optimize communication between the Flask server and the web app, all applications are put together in a Docker container. Docker is a software that allows applications to be packaged in a containerized environment, allowing the app to run on VM’s. In a final stage, Kubernetes is used to deploy the different docker containers and make them accessible through a website. The webpage is customized using HTML, CSS and Bootstrap.

How does the back-end work?

This demo is built upon the Microsoft Cognitive services API. The back-end is thus managed by the API.


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