Arabic characters
The Narrative question answering (QA) problem involves generating accurate, relevant, and human-like answers to questions based on the comprehension of a story consisting of logically connected paragraphs. However, this problem remains unexplored for the Arabic language because of the lack of Arabic narrative datasets. To address this gap, we present the Arabic-NarrativeQA dataset, which is the first dataset specifically designed for machine-reading comprehension of Arabic stories.
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AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.
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The AOLAH databases are contributions from Aswan faculty of engineering to help researchers in the field of online handwriting recognition to build a powerful system to recognize Arabic handwritten script. AOLAH stands for Aswan On-Line Arabic Handwritten where “Aswan” is the small beautiful city located at the south of Egypt, “On-Line” means that the databases are collected the same time as they are written, “Arabic” cause these databases are just collected for Arabic characters, and “Handwritten” written by the natural human hand.
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