Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritt...

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Main Authors:	Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Muhammad Zeeshan Jhanidr, Mickaël Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Gyu Sang Choi
Format:	Article
Language:	English
Published:	MDPI AG 2019-07-01
Series:	Applied Sciences
Subjects:	offline Urdu handwriting Urdu handwriting recognition convolutional neural network
Online Access:	https://www.mdpi.com/2076-3417/9/13/2758

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spelling	doaj-7b4a94e9ca1540d2a3d305895bbed42c2020-11-24T21:28:36ZengMDPI AGApplied Sciences2076-34172019-07-01913275810.3390/app9132758app9132758Recognition of Urdu Handwritten Characters Using Convolutional Neural NetworkMujtaba Husnain0Malik Muhammad Saad Missen1Shahzad Mumtaz2Muhammad Zeeshan Jhanidr3Mickaël Coustaty4Muhammad Muzzamil Luqman5Jean-Marc Ogier6Gyu Sang Choi7Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanL3i Lab, Université of La Rochelle Av. Michel Cŕepeau, 17000 La Rochelle, FranceL3i Lab, Université of La Rochelle Av. Michel Cŕepeau, 17000 La Rochelle, FranceL3i Lab, Université of La Rochelle Av. Michel Cŕepeau, 17000 La Rochelle, FranceDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 712-749, KoreaIn the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.https://www.mdpi.com/2076-3417/9/13/2758offline Urdu handwritingUrdu handwriting recognitionconvolutional neural network
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Zeeshan Jhanidr Mickaël Coustaty Muhammad Muzzamil Luqman Jean-Marc Ogier Gyu Sang Choi
spellingShingle	Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Zeeshan Jhanidr Mickaël Coustaty Muhammad Muzzamil Luqman Jean-Marc Ogier Gyu Sang Choi Recognition of Urdu Handwritten Characters Using Convolutional Neural Network Applied Sciences offline Urdu handwriting Urdu handwriting recognition convolutional neural network
author_facet	Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Zeeshan Jhanidr Mickaël Coustaty Muhammad Muzzamil Luqman Jean-Marc Ogier Gyu Sang Choi
author_sort	Mujtaba Husnain
title	Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
title_short	Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
title_full	Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
title_fullStr	Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
title_full_unstemmed	Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
title_sort	recognition of urdu handwritten characters using convolutional neural network
publisher	MDPI AG
series	Applied Sciences
issn	2076-3417
publishDate	2019-07-01
description	In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.
topic	offline Urdu handwriting Urdu handwriting recognition convolutional neural network
url	https://www.mdpi.com/2076-3417/9/13/2758
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Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

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