Comparison of LeNet-5, AlexNet and GoogLeNet models in handwriting recognition

Bartosz Michalski; Małgorzata Plechawska-Wójcik

doi:10.35784/jcsi.2919

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Published: Jun 30, 2022

DOI: https://doi.org/10.35784/jcsi.2919

Issue Vol. 23 (2022)

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Comparison of LeNet-5, AlexNet and GoogLeNet models in handwriting recognition
Bartosz Michalski, Małgorzata Plechawska-Wójcik

145-151
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DOI

https://doi.org/10.35784/jcsi.2919

Authors

Bartosz Michalski

bartosz.michalski@pollub.edu.pl

Department of Computer Science, Lublin University of Technology, Poland

Małgorzata Plechawska-Wójcik

m.plechawska@pollub.pl

Department of Computer Science, Lublin University of Technology, Poland

Abstract

The aim of the study was to compare the accuracy of handwriting recognition and the time needed to classify data from the test sets. The Lenet-5, AlexNet and GoogLeNet architectures were used for the research. They are all models of convolutional neural networks. The research was carried out with the use of image databases, handwritten digits MNIST and handwritten letters EMNIST. After the tests, it was found that the GoogLeNet model showed the highest accuracy, and the LeNet-5 the lowest. However, the LeNet-5 model needed the least time to complete the task, and GoogLeNet the most. On the basis of the obtained results, it was found that increasing the complexity of the model positively influences the accuracy of object classification, but significantly increases the demand for computer re-sources.

Keywords:

convolutional neural networks; handwriting classification

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Michalski, B., & Plechawska-Wójcik, M. (2022). Comparison of LeNet-5, AlexNet and GoogLeNet models in handwriting recognition. Journal of Computer Sciences Institute, 23, 145–151. https://doi.org/10.35784/jcsi.2919

Comparison of LeNet-5, AlexNet and GoogLeNet models in handwriting recognition

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