Bangla handwritten numeral recognition using convolutional neural network

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few wit...

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Main Authors:	Akhand, M. A. H, Rahman, Md. Mahbubar, Shill, P. C., Islam, Shahidul, Rahman, M.M. Hafizur
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/7/44250-Bangla_handwritten_numeral_recognition_using_convolutional_neural_network_Fullpaper.pdf http://irep.iium.edu.my/44250/10/44250_Bangla%20handwritten%20numeral%20recognition_Scopus.pdf

id	iium-44250
recordtype	eprints
spelling	iium-442502017-09-25T01:08:35Z http://irep.iium.edu.my/44250/ Bangla handwritten numeral recognition using convolutional neural network Akhand, M. A. H Rahman, Md. Mahbubar Shill, P. C. Islam, Shahidul Rahman, M.M. Hafizur TK Electrical engineering. Electronics Nuclear engineering Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods. IEEE 2015-05-21 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/44250/7/44250-Bangla_handwritten_numeral_recognition_using_convolutional_neural_network_Fullpaper.pdf application/pdf en http://irep.iium.edu.my/44250/10/44250_Bangla%20handwritten%20numeral%20recognition_Scopus.pdf Akhand, M. A. H and Rahman, Md. Mahbubar and Shill, P. C. and Islam, Shahidul and Rahman, M.M. Hafizur (2015) Bangla handwritten numeral recognition using convolutional neural network. In: 2nd International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2015), 21st-23rd May2015, Dhaka, Bangladesh. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7307467&tag=1 10.1109/ICEEICT.2015.7307467
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Akhand, M. A. H Rahman, Md. Mahbubar Shill, P. C. Islam, Shahidul Rahman, M.M. Hafizur Bangla handwritten numeral recognition using convolutional neural network
description	Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods.
format	Conference or Workshop Item
author	Akhand, M. A. H Rahman, Md. Mahbubar Shill, P. C. Islam, Shahidul Rahman, M.M. Hafizur
author_facet	Akhand, M. A. H Rahman, Md. Mahbubar Shill, P. C. Islam, Shahidul Rahman, M.M. Hafizur
author_sort	Akhand, M. A. H
title	Bangla handwritten numeral recognition using convolutional neural network
title_short	Bangla handwritten numeral recognition using convolutional neural network
title_full	Bangla handwritten numeral recognition using convolutional neural network
title_fullStr	Bangla handwritten numeral recognition using convolutional neural network
title_full_unstemmed	Bangla handwritten numeral recognition using convolutional neural network
title_sort	bangla handwritten numeral recognition using convolutional neural network
publisher	IEEE
publishDate	2015
url	http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/ http://irep.iium.edu.my/44250/7/44250-Bangla_handwritten_numeral_recognition_using_convolutional_neural_network_Fullpaper.pdf http://irep.iium.edu.my/44250/10/44250_Bangla%20handwritten%20numeral%20recognition_Scopus.pdf
first_indexed	2023-09-18T21:02:56Z
last_indexed	2023-09-18T21:02:56Z
_version_	1777410744385536000

Bangla handwritten numeral recognition using convolutional neural network

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