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Improving the accuracy of Arabic text recognition in imagery requires a big modern dataset as data is the fuel for many modern machine learning models. This paper proposes a new dataset, called QTID, for Quran Text Image Dataset, the first Arabic dataset that includes Arabic marks. It consists of 309,720 different 192x64 annotated Arabic word images that contain 2,494,428 characters in total, which were taken from the Holy Quran. These finely annotated images were randomly divided into 90%, 5%, 5% sets for training, validation, and testing, respectively. In order to analyze QTID, a different dataset statistics were shown. Experimental evaluation shows that current best Arabic text recognition engines like Tesseract and ABBYY FineReader cannot work well with word images from the proposed dataset.

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Testing is one of the vital stages in the software development life cycle (SDLC). Usability testing is a very important field that helps the applications be easily used by the end-users. Because of the importance of usability testing, a metrics has been developed to help in measuring the usability through converting the main qualitative usability attributes in ISO to quantitative steps that provide the developer a framework to follow in developing to achieve usability of their applications and helps the tester with a checklist and a tool to measure the usability percentage of their application. The framework provides a set of steps to achieve the usability attributes and answers the question of how you could measure this attribute with the defined steps. The framework results in a 95% average accuracy in the high-rate application and a 59% average accuracy in the low-rate application. Finally, the framework is programmed in a tool to measure the usability percentage of the application through a checklist and provides a scheme to help the developer achieve the best results in usability.

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