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Mohd Afiq Zamanhuri
Universiti Teknologi MARA

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Deep Learning for Roman Handwritten Character Recognition Muhaafidz Md Saufi; Mohd Afiq Zamanhuri; Norasiah Mohammad; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp455-460

Abstract

The advantage of deep learning is that the analysis and learning of massive amounts of unsupervised data make it a beneficial tool for Big Data analysis. Convolution Neural Network (CNN) is a deep learning method that can be used to classify image, cluster them by similarity, and perform image recognition in the scene. This paper conducts a comparative study between three deep learning models, which are simple-CNN, AlexNet and GoogLeNet for Roman handwritten character recognition using Chars74K dataset. The produced results indicate that GooleNet achieves the best accuracy but it requires a longer time to achieve such result while AlexNet produces less accurate result but at a faster rate.
Co-Authors Muhaafidz Md Saufi Norasiah Mohammad Zaidah Ibrahim