Azure OCR (OCR Engine)

WIP	This article is a work-in-progress or created as a placeholder for testing purposes. This article is subject to change and/or expansion. It may be incomplete, inaccurate, or stop abruptly. This tag will be removed upon draft completion.

This article is about the current version of Grooper. Note that some content may still need to be updated.

2025

Azure OCR is an OCR Engine option for OCR Profiles that utilizes Microsoft Azure's Read API. Azure's Read engine is an AI-based text recognition software that uses a convolutional neural network (CNN) to recognize text. Compared to traditional OCR engines, it yields superior results, especially for handwritten text and poor quality images. Furthermore, Grooper supplements Azure's results with those from a traditional OCR engine in areas where traditional OCR is better than the Read engine.

You may download the ZIP(s) below and upload it into your own Grooper environment (version 2024). The first contains one or more Batches of sample documents. The second contains one or more Projects with resources used in examples throughout this article. Media:2024_Wiki_Azure-OCR_Batch.zip Media:2024_Wiki_Azure-OCR_Project.zip

About

Azure OCR is different from traditional OCR Engines. It is a CNN (Convolutional Neural Network) based OCR Engine meaning that it is AI based. Due to the way this neural network has been trained, Azure OCR is less dependent on Image Processing.

Unlike traditional OCR, Azure OCR has a far higher accuracy when recognizing handwritten text on documents. However, Azure OCR alone does not give 100% accurate position data of characters, it only gives us an approximation. This can cause problems for extractors that are reliant on character/text positions such as Labeled Value, Labeled OMR, or Tabular Layout. Azure OCR also does not always capture smaller numeric values such as 1s and 0s. This can make collecting some data problematic.

To compensate, a traditional OCR Engine (Transym) runs at the same time when using Azure OCR because traditional OCR is highly effective at obtaining position data and can capture smaller values. A traditional OCR Engine is more dependent on Image Processing. When choosing Azure OCR, a default set of Image Processing steps are applied to the document so the traditional OCR Engine to improve OCR accuracy.

Grooper attempts to return the most accurate results from both the Azure OCR and the traditional OCR Engine.

Traditional OCR vs. Azure OCR

In the screenshots below, we can see the difference between using traditional OCR and Azure OCR on a document that has small text and handwriting.

1. In the first screenshot, we can see the result of using traditional OCR. The traditional OCR is not equipped to handle handwriting, and the small print with minimal spaces between the characters makes it very difficult for traditional OCR.

2. In this second screenshot, we have used Azure OCR on the same document. Azure OCR relies on the CNN AI training rather than individual character analysis. Azure OCR does a much better job at returning accurate data for this particular document, even the handwritten sections.

How To

To use Azure OCR you will need to add and configure an OCR Profile. Then you will need to add that OCR Profile to your Recognize Batch Process Step. Then you can test your Step or run the Batch Process when complete.

Setting up the OCR Profile

Adding an OCR Profile

1. Right-click on the Project or folder inside of your Project in your Node Tree where you want to add your OCR Profile.
2. Hover over "Add".
3. Click on "OCR Profile..."

4. Enter in your desired name for your OCR Profile in the Name property field.
5. Click "EXECUTE" in the top right-hand corner of the pop-up window to create your OCR Profile.

6. Now you should have a new OCR Profile in your Node Tree.

Configuring the OCR Profile

1. Click the hamburger icon to the right of the OCR Engine property to access the drop down menu.
2. Select Azure OCR from the drop down menu.

3. Copy and paste your unique API Key into the API Key property and select your API Region from the drop down menu accessed by clicking on the hamburger icon next to the property.
4. Optionally, you can add a Traditional Ocr Profile. If this property is left blank, Grooper will run a basic Traditional OCR Profile that is default for Azure OCR. If you would like to override the default, you can select a different OCR Profile here.
5. Click the save icon in the top right of the property grid to save your changes.

Adding the OCR Profile to the Recognize Step

You will need to add a Batch Process Step configured with the Recognize Activity to your Batch Process. You will also need to configure the Step Properties such as the Activity and Scope. For help with setting up your Batch Process, take a look at our Batch Process article.

1. Add and select the Recognize Step in your Batch Process in the Node Tree.
2. Click on the hamburger icon to the right of the OCR Profile property to access the navigation drop down.
3. Navigate to and select the OCR Profile that has been configured with the Azure OCR OCR Engine.

4. Finish configuring your Batch Process Step and then click the save icon located in the top right of the Step Properties property grid to save your changes.