Handwritten Bangla numeral recognition using deep long short term memory

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

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Main Authors:	Ahmed, Mahtab, Akhand, M. A. H, Rahman, M.M. Hafizur
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. ( IEEE) 2017
Subjects:	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Online Access:	http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/1/54935_Handwritten%20Bangla%20numeral%20recognition.pdf http://irep.iium.edu.my/54935/2/54935_Handwritten%20Bangla%20numeral%20recognition_SCOPUS.pdf

id	iium-54935
recordtype	eprints
spelling	iium-549352017-06-05T01:01:52Z http://irep.iium.edu.my/54935/ Handwritten Bangla numeral recognition using deep long short term memory Ahmed, Mahtab Akhand, M. A. H Rahman, M.M. Hafizur TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Bangla is a major language in Indian subcontinent and is the first language of Bangladesh; but unfortunately, study regarding handwritten Bangla numeral recognition (HBNR) is very few with respect to other major languages such as English, Roman etc. Some noteworthy research works have been conducted for recognition of Bangla handwritten numeral using artificial neural network (ANN) as ANN and its various updated models are found efficient for classification task. The aim of this study is to develop a better Bangla handwritten numeral recognition system and hence investigated deep architecture of Long Short Term Memory (LSTM) method. LSTM is a variant of recurrent neural networks (RNN) and is applied efficiently for image classification with its distinct features. The proposed HBNR-LSTM normalizes the written numeral images first and then employs two layers of LSTM to classify individual numerals. Unlike other methods, it does not employ any feature extraction technique. Benchmark dataset with 22000 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. Institute of Electrical and Electronics Engineers Inc. ( IEEE) 2017-01-16 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/54935/1/54935_Handwritten%20Bangla%20numeral%20recognition.pdf application/pdf en http://irep.iium.edu.my/54935/2/54935_Handwritten%20Bangla%20numeral%20recognition_SCOPUS.pdf Ahmed, Mahtab and Akhand, M. A. H and Rahman, M.M. Hafizur (2017) Handwritten Bangla numeral recognition using deep long short term memory. In: 2016 6th International Conference on Information and Communication Technology for The Muslim World, Nov. 22 ~ 24, 2016, Jakarta, Indonesia. http://ieeexplore.ieee.org/document/7814922/ 10.1109/ICT4M.2016.62
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
spellingShingle	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Ahmed, Mahtab Akhand, M. A. H Rahman, M.M. Hafizur Handwritten Bangla numeral recognition using deep long short term memory
description	Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Bangla is a major language in Indian subcontinent and is the first language of Bangladesh; but unfortunately, study regarding handwritten Bangla numeral recognition (HBNR) is very few with respect to other major languages such as English, Roman etc. Some noteworthy research works have been conducted for recognition of Bangla handwritten numeral using artificial neural network (ANN) as ANN and its various updated models are found efficient for classification task. The aim of this study is to develop a better Bangla handwritten numeral recognition system and hence investigated deep architecture of Long Short Term Memory (LSTM) method. LSTM is a variant of recurrent neural networks (RNN) and is applied efficiently for image classification with its distinct features. The proposed HBNR-LSTM normalizes the written numeral images first and then employs two layers of LSTM to classify individual numerals. Unlike other methods, it does not employ any feature extraction technique. Benchmark dataset with 22000 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	Ahmed, Mahtab Akhand, M. A. H Rahman, M.M. Hafizur
author_facet	Ahmed, Mahtab Akhand, M. A. H Rahman, M.M. Hafizur
author_sort	Ahmed, Mahtab
title	Handwritten Bangla numeral recognition using deep long short term memory
title_short	Handwritten Bangla numeral recognition using deep long short term memory
title_full	Handwritten Bangla numeral recognition using deep long short term memory
title_fullStr	Handwritten Bangla numeral recognition using deep long short term memory
title_full_unstemmed	Handwritten Bangla numeral recognition using deep long short term memory
title_sort	handwritten bangla numeral recognition using deep long short term memory
publisher	Institute of Electrical and Electronics Engineers Inc. ( IEEE)
publishDate	2017
url	http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/ http://irep.iium.edu.my/54935/1/54935_Handwritten%20Bangla%20numeral%20recognition.pdf http://irep.iium.edu.my/54935/2/54935_Handwritten%20Bangla%20numeral%20recognition_SCOPUS.pdf
first_indexed	2023-09-18T21:17:40Z
last_indexed	2023-09-18T21:17:40Z
_version_	1777411671252271104

Handwritten Bangla numeral recognition using deep long short term memory

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