Ghanim, M., Mohammed, A., Sali, A. (2023). Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF. Alustath, 28(2), 252-260. doi: 10.33899/rengj.2023.138592.1236
M. F. Ghanim; Amena Mohammed; Aduwati Sali. "Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF". Alustath, 28, 2, 2023, 252-260. doi: 10.33899/rengj.2023.138592.1236
Ghanim, M., Mohammed, A., Sali, A. (2023). 'Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF', Alustath, 28(2), pp. 252-260. doi: 10.33899/rengj.2023.138592.1236
Ghanim, M., Mohammed, A., Sali, A. Arabic/English Handwritten Digits Recognition using MLPs, CNN, RF, and CNN-RF. Alustath, 2023; 28(2): 252-260. doi: 10.33899/rengj.2023.138592.1236

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

Al-Rafidain Engineering Journal (AREJ)
Volume 28, Issue 2, September 2023, Pages 252-260    PDF (613.96 K)
Document Type: Research Paper
DOI: 10.33899/rengj.2023.138592.1236
Authors
M. F. Ghanim* 1; Amena Mohammed1; Aduwati Sali2
1Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq
2WiPNET Research Centre, Dept. of Computer and Communication Systems Engineering, Faculty of Engineering, UPM, Selangor, Malaysia
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%.
Keywords
CNNs; Neural Network; Handwritten Digit Recognition; MNIST & MADBase datasets; random forest (RF)
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