Deep Learning for Roman Handwritten Character Recognition
Main Authors: | Muhaafidz Md Saufi, Mohd Afiq Zamanhuri, Norasiah Mohammad, Zaidah Ibrahim |
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Format: | Article Journal |
Terbitan: |
, 2018
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Subjects: | |
Online Access: |
https://zenodo.org/record/4314254 |
Daftar Isi:
- 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.