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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number4
Pages (from-to)420-436
Number of pages17
JournalInternational Journal of Computational Vision and Robotics
Volume13
Issue number4
DOIs
StatePublished - 2023

Keywords

  • CNNs
  • comparative study
  • convolution neural networks
  • deep learning
  • hand-written digit
  • MLP
  • multi-layer perceptron
  • pattern recognition

Fingerprint

Dive into the research topics of 'A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition'. Together they form a unique fingerprint.

Cite this