Handwritten Arabic Digit Recognition Using Convolutional Neural Network

6Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

Abstract

In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy. The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising.

Author supplied keywords

Cite

CITATION STYLE

APA

Alkhateeb, J. H. (2020). Handwritten Arabic Digit Recognition Using Convolutional Neural Network. International Journal of Communication Networks and Information Security, 12(3), 411–416. https://doi.org/10.17762/ijcnis.v12i3.4807

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free