Title: A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition

Authors: Aaron Rasheed Rababaah

Addresses: Department of Computer Science and Information Systems, American University of Kuwait (AUK), Kuwait

Abstract: This paper presents an investigation that aims at comparing deep learning (DL) and traditional artificial neural networks (ANNs) in the application of hand-written digits recognition (HDR). In our study, convolution neural networks (CNNs) are a representative model for the DL models and the multi-layer perceptron (MLP) is a representative model for ANN models. The two models MLP and CNN were implemented using MATLAB development environment and tested using a publicly available image database. The databse consists of over 20,000 samples with all ten hand-written digits each of which is 24 × 24 pixels. The experimental results showed that the CNN model was superior to the MLP model with an average classification accuracy of 95.14% and 89.74% respectively. Furthermore, the CNN model was observed to have better performance stability and better execution efficiency as the MLP model requires human intervention to handcraft and pre-process the features of the digit patterns.

Keywords: hand-written digit; pattern recognition; multi-layer perceptron; MLP; deep learning; convolution neural networks; CNNs; comparative study.

DOI: 10.1504/IJCVR.2023.131985

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.4, pp.420 - 436

Received: 22 Feb 2022
Accepted: 10 Apr 2022
Published online: 06 Jul 2023 *

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