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Prof. Ahmed Abouelyazed Elsawy Ali :: Publications:

Title:
Arabic Handwritten Characters Recognition Using Convolutional Neural Network
Authors: Ahmed El-Sawy, Mohamed Loey, Hazem EL-Bakry
Year: 2017
Keywords: Arabic Character Recognition, Deep Learning, Convolutional Neural Network
Journal: WSEAS Transactions on Computer Research
Volume: 5
Issue: Not Available
Pages: 11-19
Publisher: WSEAS Transactions on Computer Research
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and large public databases. In this work, we model a deep learning architecture that can be effectively apply to recognizing Arabic handwritten characters. A Convolutional Neural Network (CNN) is a special type of feed-forward multilayer trained in supervised mode. The CNN trained and tested our database that contain 16800 of handwritten Arabic characters. In this paper, the optimization methods implemented to increase the performance of CNN. Common machine learning methods usually apply a combination of feature extractor and trainable classifier. The use of CNN leads to significant improvements across different machine-learning classification algorithms. Our proposed CNN is giving an average 5.1% misclassification error on testing data

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