Azure OCR (OCR Engine)
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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. |
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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.
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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. |
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.