RECOGNITION OF ARABIC HANDWRITTEN CHARACTERS USING RESIDUAL NEURAL NETWORKS
Ahmad T. Al- Taani, Sadeem T. Ahmad.
Abstract
This study proposes the use of Residual Neural Networks (ResNets) to recognise Arabic offline isolated handwritten characters including Arabic digits. ResNets is a deep learning approach which showed effectiveness in many applications more than conventional machine learning approaches. The proposed approach consists of three main phases: pre-processing phase, training the ResNet on the training set, and testing the trained ResNet on the datasets. The evaluation of the proposed approach is performed on three available datasets: MADBase, AIA9K, and AHCD. The proposed approach achieved accuracies of 99.8%, 99.05% and 99.55% on these datasets, respectively. It also achieved a validation accuracy of 98.9% on the constructed dataset based on the three datasets.
Key words: Residual Network, Deep Learning, Deep Neural Networks, Arabic Handwritten, Characters Recognition.
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