Arabic handwritten character recognition based on deep convolutional neural networks
Author
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 3, Issue 3 (31 Dec. 2017), pp.186-200, 15 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2017-12-31
Country of Publication
Jordan
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
The automatic analysis and recognition of offline Arabic handwritten characters from images is an important problem in many applications.
even with the great progress of recent research in optical character recognition, a few problems still wait to be solved, especially for Arabic characters.
the emergence of deep neural networks promises a strong solution to some of these problems.
we present a deep neural network for the handwritten Arabic character recognition problem that uses convolutional neural network (CNN) models with regularization parameters such as batch normalization to prevent overfitting.
we applied the Deep CNN for the AIA9k and the AHCD databases and the classification accuracies for the two datasets were 94.8% and 97.6%, respectively.
a study of the network performance on the EMNIST and a form-based AHCD dataset were performed to aid in the analysis.
American Psychological Association (APA)
Yunus, Khalid S.. 2017. Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian Journal of Computetrs and Information Technology،Vol. 3, no. 3, pp.186-200.
https://search.emarefa.net/detail/BIM-1415323
Modern Language Association (MLA)
Yunus, Khalid S.. Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian Journal of Computetrs and Information Technology Vol. 3, no. 3 (Dec. 2017), pp.186-200.
https://search.emarefa.net/detail/BIM-1415323
American Medical Association (AMA)
Yunus, Khalid S.. Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian Journal of Computetrs and Information Technology. 2017. Vol. 3, no. 3, pp.186-200.
https://search.emarefa.net/detail/BIM-1415323
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 198-200
Record ID
BIM-1415323