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 language | English |
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Article number | 4 |
Pages (from-to) | 420-436 |
Number of pages | 17 |
Journal | International Journal of Computational Vision and Robotics |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - 2023 |
Keywords
- CNNs
- comparative study
- convolution neural networks
- deep learning
- hand-written digit
- MLP
- multi-layer perceptron
- pattern recognition