Arabic handwritten alphanumeric character recognition using very deep neural network

被引：31

作者：

Mudhsh M. ^{[1
]}

Almodfer R. ^{[1
]}

机构：

[1] School of Computer Science, Wuhan University of Technology, Luo Shi Road, Wuhan

来源：

Information (Switzerland) | 2017年 / 8卷 / 03期

关键词：

Alphanumeric recognition; Arabic handwritten; Augmentation; Deep learning; Dropout; VGGNet;

D O I：

10.3390/info8030105

中图分类号：

学科分类号：

摘要：

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. © 2017 by the authors.

引用

共 33 条

[1]

Ashiquzzaman A., Tushar A.K., Handwritten Arabic numeral recognition using deep learning neural networks, Proceedings of the 2017 IEEE International Conference on Imagin, pp. 1-4, (2017)

[2]

Chergui L., Kef M., SIFT descriptors for Arabic handwriting recognition, Int. J. Comput. Vis. Robot., 5, pp. 441-461, (2015)

[3]

Assayony M.O., Mahmoud S.A., An Enhanced Bag-of-Features Framework for Arabic Handwritten Sub-words and Digits Recognition, J. Pattern Recognit. Intell. Syst., 4, pp. 27-38, (2016)

[4]

Tharwat A., Gaber T., Hassanien A.E., Shahin M., Refaat B., Sift-based arabic sign language recognition system, Afro-european Conference for Industrial Advancement

[5]

Springer: Berlin, pp. 359-370, (2015)

[6]

Chen J., Cao H., Prasad R., Bhardwaj A., Natarajan P., Gabor features for offline Arabic handwriting recognition, Proceedings of the 9th IAPR International Workshop on Document Analysis System, pp. 53-58, (2010)

[7]

Elzobi M., Al-Hamadi A., Saeed A., Dings L., Arabic handwriting recognition using gabor wavelet transform and SVM, Proceedings of the 2012 IEEE 11th International Conference on Signal Processing (ICSP, pp. 2154-2158, (2012)

[8]

Elleuch M., Tagougui N., Kherallah M., Towards Unsupervised Learning for Arabic Handwritten Recognition Using Deep Architectures, Proceedings of the International Conference on Neural Infomation Processin, pp. 363-372, (2015)

[9]

Zaiz F., Babahenini M.C., Djeffal A., Puzzle based system for improving Arabic handwriting recognition, Eng. Appl. Artif. Intell., 56, pp. 222-229, (2016)

[10]

Pechwitz M., Maddouri S.S., Margner V., Ellouze N., Amiri H., IFN/ENIT-database of handwritten Arabic words, Proceedings of the 7th Colloque International Francophone sur l'Ecrit et le Document (CIFED, pp. 127-136, (2002)

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