Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF

Section: Research Paper
Issue
Vol. 28 No. 2 (2023): Volume 28 Issue 2
Published
Sep 1, 2023
Pages
252-260

Abstract

Day by day, machine learning and deep learning reduce the efforts needed by humans in many fields. Handwriting recognition is one such field. In Handwriting Recognition (HWR), a machine can interpret and recognize handwritten input from different sources like papers, touch screens, images, etc. by interpreting it into machine-readable formats. Arab countries often use Arabic digits in addition to English digits. In banks, business applications, etc. This article discusses four methods to recognize Arabic/English handwritten digits which are: random forest (RF), multi-layer perceptrons (MLPs), convolutional neural network (CNN), and CNN-RF. These methods were implemented with the help of the MNIST and MADBase datasets and the results appear that in comparison with the other algorithms, the highest accuracy was obtained by the Convolutional Neural Network (CNN) with a value of 99.11%.

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Authors

M. F. Ghanim

Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq

ORCID
Amena Mohammed

WiPNET Research Centre, Dept. of Computer and Communication Systems Engineering, Faculty of Engineering, UPM, Selangor, Malaysia

ORCID
Aduwati Sali

-

ORCID

Identifiers

https://doi.org/10.33899/rengj.2023.138592.1236
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How to Cite

[1]
M. F. Ghanim, A. Mohammed, and A. Sali, “Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF”, AREJ, vol. 28, no. 2, pp. 252–260, Sep. 2023.
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