Convolutional Neural Network Model for Arabic Handwritten Characters Recognition
Murtada Khalafallah Elbashir, Mohamed Elhafiz Mustafa · IJARCCE · 2018
In this paper, we presented a Convolutional Neural Network (CNN) model for off-line Arabic handwritten character recognition.The proposed CNN model used the dataset which prepared by Sudan University of Science and Technology-Arabic Language Technology group.The dataset is pre-processed before feeding it to the CNN model.In the pre-processing, all the characters images are size normalized to fit in a 20 by 20 pixel and then centred in a scaled images of size 28×28 pixel using the centre of mass then all the images are converted to be having a black background and white foreground colours.The pre-processed images are fed to the CNN model, which is constructed using the sequential model of the Keras library under tensorflow environment.The accuracy obtained varied from 93.5% as test accuracy to 97.5% as training accuracy showing better results than other methods that used the same dataset.