Development of English handwritten recognition using deep neural network

Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and...

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Main Authors:	Gunawan, Teddy Surya, Mohd Noor, Ahmad Fakhrur Razi, Kartiwi, Mira
Format:	Article
Language:	English English
Published:	IAES 2018
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/1/11766-16196-3-PBTeddyDeepHandwritten.pdf http://irep.iium.edu.my/62496/7/62496%20Development%20of%20English%20Handwritten%20Recognition%20Using%20SCOPUS.pdf

id	iium-62496
recordtype	eprints
spelling	iium-624962018-03-27T08:28:27Z http://irep.iium.edu.my/62496/ Development of English handwritten recognition using deep neural network Gunawan, Teddy Surya Mohd Noor, Ahmad Fakhrur Razi Kartiwi, Mira TK7885 Computer engineering Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy. IAES 2018-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62496/1/11766-16196-3-PBTeddyDeepHandwritten.pdf application/pdf en http://irep.iium.edu.my/62496/7/62496%20Development%20of%20English%20Handwritten%20Recognition%20Using%20SCOPUS.pdf Gunawan, Teddy Surya and Mohd Noor, Ahmad Fakhrur Razi and Kartiwi, Mira (2018) Development of English handwritten recognition using deep neural network. Indonesian Journal of Electrical Engineering and Computer Science, 10 (2). pp. 562-568. ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/view/11766/8302 10.11591/ijeecs.v10.i2.pp562-568
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Gunawan, Teddy Surya Mohd Noor, Ahmad Fakhrur Razi Kartiwi, Mira Development of English handwritten recognition using deep neural network
description	Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy.
format	Article
author	Gunawan, Teddy Surya Mohd Noor, Ahmad Fakhrur Razi Kartiwi, Mira
author_facet	Gunawan, Teddy Surya Mohd Noor, Ahmad Fakhrur Razi Kartiwi, Mira
author_sort	Gunawan, Teddy Surya
title	Development of English handwritten recognition using deep neural network
title_short	Development of English handwritten recognition using deep neural network
title_full	Development of English handwritten recognition using deep neural network
title_fullStr	Development of English handwritten recognition using deep neural network
title_full_unstemmed	Development of English handwritten recognition using deep neural network
title_sort	development of english handwritten recognition using deep neural network
publisher	IAES
publishDate	2018
url	http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/ http://irep.iium.edu.my/62496/1/11766-16196-3-PBTeddyDeepHandwritten.pdf http://irep.iium.edu.my/62496/7/62496%20Development%20of%20English%20Handwritten%20Recognition%20Using%20SCOPUS.pdf
first_indexed	2023-09-18T21:28:34Z
last_indexed	2023-09-18T21:28:34Z
_version_	1777412356641390592

Development of English handwritten recognition using deep neural network

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