Open AccessDeep Learning for Roman Handwritten Character Recognition
Author(s) -
Muhaafidz Md Saufi,
Mohd Afiq Bin Zamanhuri,
Norasiah Mohammad,
Zarina Bibi İbrahim
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v12.i2.pp455-460
Subject(s) - computer science , deep learning , artificial intelligence , convolutional neural network , pattern recognition (psychology) , character (mathematics) , similarity (geometry) , convolution (computer science) , character recognition , image (mathematics) , artificial neural network , mathematics , geometry
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.