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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110819
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.
Abstract: Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art.
Keywords: Handwritten Arabic recognition; convolutional neural networks; support vector machine
Mahmoud Shams, Amira. A. Elsonbaty and Wael. Z. ElSawy, “Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110819
@article{Shams2020,
title = {Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110819},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110819},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Mahmoud Shams and Amira. A. Elsonbaty and Wael. Z. ElSawy}
}
