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Automatic Arabic handwritten recognition is one of the recently studiedproblems in the field of Machine Learning. Unlike Latin languages, Arabic is aSemitic language that forms a harder challenge, especially with variability ofpatterns caused by factors such as writer age. Most of the studies focused onadults, with only one recent study on children. Moreover, much of the recentMachine Learning methods focused on using Convolutional Neural Networks, apowerful class of neural networks that can extract complex features fromimages. In this paper we propose a convolutional neural network (CNN) modelthat recognizes children handwriting with an accuracy of 91% on the Hijjadataset, a recent dataset built by collecting images of the Arabic characterswritten by children, and 97% on Arabic Handwritten Character Dataset. Theresults showed a good improvement over the proposed model from the Hijjadataset authors, yet it reveals a bigger challenge to solve for children Arabichandwritten character recognition. Moreover, we proposed a new approach usingmulti models instead of single model based on the number of strokes in acharacter, and merged Hijja with AHCD which reached an averaged predictionaccuracy of 96%.

Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification