You have tried nearly everything. You tried learning, that didn't work. You tried simulating the object, but it is moving around. All hope is lost. Here he comes to save the day, that means UnderDog is on the way!!!In this case, underdog, or the hero, may be OCR. Rapise has many built in capabilities to facilitate the learning and identification of objects used in a test, and OCR is one of them. But what is OCR anyway?
OCR stands for Optical Character Recognition. I won't go into specific details, but provide a high level overview. OCR takes the object, area, or window and converts it to an image. OCR then scans this image for patterns it recognizes as part of a known character set. Sounds sketchy? Not as much as you might think.
OCR has not always had a great reputation, and think about it, we had underpowered computers that could barely keep up with the tasks at hand, much less power operations such as image processing and character recognition. With modern systems and algorithms, this task has become much better. It is still not perfect but in a pinch it can be incredibly useful.
If you intend to use OCR as a part of your testing, you MUST turn off font smoothing as this is a display layer smoothing that will throw off the OCR capabilities in ANY OCR reliant process weather it be from Rapise or other commercial grade OCR tool.
With OCR you can identify a known string in an image or area. You can identify the points that form the string and interact with them. You can capture values and a host of other activities.
To get to the OCR commands, expand the Global item in the Object tree, within Global expand OCR and you can drag and drop the following commands into your test and add parameters:
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