Text recognition in run-time

Relevant for: GUI tests and components

OpenText Functional Testing uses optical character recognition (OCR) mechanisms to identify text in your application.

When OpenText Functional Testing uses OCR

You can use OpenText Functional Testing's OCR capabilities in the following situations:

When working with tests and scripted components, you can use the text and text area checkpoint or output value commands to verify or retrieve text in your objects.
In addition, when working with tests, keyword or scripted components, and function libraries, you can insert steps to capture the text from objects in your application using the .GetVisibleText, the .GetTextLocation test object methods, the TextUtil.GetText or TextUtil.GetTextLocation reserved object methods, or the .GetText (for Terminal Emulator objects).
Text test objects, which represent specific texts in the application, are also identified using OCR.

Note: Text recognition is not supported for objects in the Active Screen.

To improve your test's performance, OpenText Functional Testing caches the texts retrieved from images by OCR engines within each test run.

Tip: You can change the OCR settings during a test run, for example, using SetABBYYParameters. If you need these changes to affect images whose texts was previously retrieved in the test run, you need to clear the OCR cache first. For details, see ClearOCRCache in Object Model Reference.

Text test objects

Create Text test objects to represent specific texts in your application, regardless of the technology used to develop the application. You can then perform operations on these test objects, such as Click, Drag, Drop, and Hover.

You can add Text test objects in the object repository editors and during a recording session. For details, see Work with Insight or Text test objects and Record a Text object step.

For the operations and properties supported on TextObjects, see the Insight & Text > TextObject Object topic in the Object Model Reference.

OCR mechanisms and settings

When the OCR mechanism is used, a number of factors can affect the text it retrieves. Depending on the characteristics of the text you want to retrieve, you can adjust several OCR configuration options to optimize the way the text is captured. Use the Configure text recognition settings to specify the preferred text recognition mechanism and OCR-specific settings.

You can use one of the following text recognition engines:

The ABBYY OCR (the default option)
The Tesseract OCR engine
The Google Cloud OCR engine
The Baidu Cloud OCR engine

Note:

Using a cloud OCR engine requires setting up an account with the relevant vendor and obtaining an access token or key used to connect to the cloud service.
If OpenText Functional Testing cannot connect to the cloud OCR service using the configured details, it uses ABBYY instead.
To use the ABBYY OCR Engine for text recognition, you must include it when installing OpenText Functional Testing. If ABBYY is not installed, Tesseract is used as the default engine instead.

Optimize text recognition

OCR's accuracy depends on font and image quality and uniformity.

You should also note the following considerations for performing more effective text recognition:

Parameters	Description
Fonts in your text	(For the ABBYY and Tesseract OCR engines only) Single text block mode and multiple text block mode sometimes result in different captured text. If you are not sure which text block mode to use, use the default multiple block mode. If the results are not what you expect, then try using the single text block mode. If you want to use the text recognition mechanism for a large area containing different fonts and backgrounds, we recommend creating several steps to capture the text for each single text block instead of creating one step to capture a multiple text block. If the text recognition mechanism retrieves unwanted text information (such as hidden text and shadowed text that appears as multiple copies of the same string) when using the multiple text block mode, use the single text block mode option. To do this, in the Text Recognition pane of the Options dialog box (Tools > Options > GUI Testing tab > Text Recognition node), select Single text block mode. If your application uses small fonts (less than 10 pt.) you should use the Tesseract OCR engine with the Preprocess the image before using text recognition option selected.
Colors and color contrast	The color scheme of the background should be permanent and without gradient. High contrast between the background and text is best for text recognition (for example, black text on a white background).
Text within images	If your text is found within an image, we recommend using the Preprocess the image before using text recognition option in the Text Recognition pane of the Options dialog box (Tools > Options > GUI Testing tab > Text Recognition mode).
Dimension for text recognition	Try to keep the dimensions of the selected text area as small as possible to prevent additional unwanted characters in recognized text. At the same time, if you are using a cloud vendor, avoid very small windows or elements and do not specify such a small area for a text area checkpoint that the recognition fails: When using Baidu: Make sure that your rectangle's length and width are no shorter than 15 pixels and no longer than 4096 pixels. When using Google: Make sure that your rectangle's length and width are no longer than 20 Megapixels. Recommended size: 1024 x 768. Consider the potential movement (change of coordinates) of the object within the window. For example, the screen resolution is often different on different computers, and this can affect the coordinates of the object in the application. Also, during the design and development stages of an application, an object may be moved to make room for other objects or for aesthetic purposes. Consider that the operating system, installed service packs, or installed toolkits, can all affect the size and location of an object in an application. Make sure that the dimensions of the selected text area are large enough for different system configurations. The size of the application window also affects the text recognition. Experiment with application window size to achieve the best recognition. Then, make sure to record and run your tests on that size.
Tests created in UFT 15.0 or earlier	OpenText Functional Testing now uses an ABBYY OCR engine that is newer than it used before. As a result, you may see changes in the text recognition of tests created in OpenText Functional Testing 15.0 or earlier.
OCR Engine consistency	After you determine which OCR engine works best with your tests, we recommend using that engine consistently. Using different engines for different runs may produce different results.
Recognizing unusual text characters	If you are using the ABBYY OCR engine, you can train it to identify unusual or unclear characters in your application. For details, see ABBYY OCR Pattern Training.