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...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc. ( IEEE)
2017
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| Online Access: | 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 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T16:38:08Z |
| format | Proceeding Paper |
| id | iium-54935 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:38:08Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers Inc. ( IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | Handwritten Bangla numeral recognition using deep long short term memory |
| title_sort | handwritten bangla numeral recognition using deep long short term memory |
| topic | TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| 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 |