Deep Learning for Roman Handwritten Character Recognition

Muhaafidz Md Saufi (Universiti Teknologi MARA)
Mohd Afiq Zamanhuri (Universiti Teknologi MARA)
Norasiah Mohammad (Universiti Teknologi MARA)
Zaidah Ibrahim (Universiti Teknologi MARA)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 12, No 2: November 2018

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.