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Information 2017, 8(3), 105; doi:10.3390/info8030105

Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network

School of Computer Science, Wuhan University of Technology, Luo Shi Road, Wuhan 430070, China
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Received: 5 August 2017 / Revised: 23 August 2017 / Accepted: 25 August 2017 / Published: 31 August 2017
(This article belongs to the Section Artificial Intelligence)
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Abstract

The traditional algorithms for recognizing handwritten alphanumeric characters are dependent on hand-designed features. In recent days, deep learning techniques have brought about new breakthrough technology for pattern recognition applications, especially for handwritten recognition. However, deeper networks are needed to deliver state-of-the-art results in this area. In this paper, inspired by the success of the very deep state-of-the-art VGGNet, we propose Alphanumeric VGG net for Arabic handwritten alphanumeric character recognition. Alphanumeric VGG net is constructed by thirteen convolutional layers, two max-pooling layers, and three fully-connected layers. The proposed model is fast and reliable, which improves the classification performance. Besides, this model has also reduced the overall complexity of VGGNet. We evaluated our approach on two benchmarking databases. We have achieved very promising results, with a validation accuracy of 99.66% for the ADBase database and 97.32% for the HACDB database. View Full-Text
Keywords: alphanumeric recognition; Arabic handwritten; deep learning; VGGNet; dropout; augmentation alphanumeric recognition; Arabic handwritten; deep learning; VGGNet; dropout; augmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mudhsh, M.; Almodfer, R. Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network. Information 2017, 8, 105.

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