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In this paper, we introduce the first phase of a new dataset for offlineArabic handwriting recognition. The aim is to collect a very large dataset ofisolated Arabic words that covers all letters of the alphabet in all possibleshapes using a small number of simple words. The end goal is to collect a verylarge dataset of segmented letter images, which can be used to build andevaluate Arabic handwriting recognition systems that are based on segmentedletter recognition. The current version of the dataset contains $25114$ samplesof $109$ unique Arabic words that cover all possible shapes of all alphabetletters. The samples were collected from $907$ writers. In its current form,the dataset can be used for the problem of closed-vocabulary word recognition.We evaluated a number of window-based descriptors and classifiers on this taskand obtained an accuracy of $92.16\%$ using a SIFT-based descriptor and ANN.

AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition