Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition
Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Ban...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Online Access: | http://irep.iium.edu.my/53467/ http://irep.iium.edu.my/53467/7/53467.pdf http://irep.iium.edu.my/53467/13/53467_Convolutional%20neural%20network%20training%20with%20artificial%20pattern_SCOPUS%202016.pdf http://irep.iium.edu.my/53467/19/53467_Convolutional%20neural%20network%20training_WOS.pdf |
| _version_ | 1848784235519803392 |
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| author | Akhand, M. A. H Ahmed, Mahtab Rahman, M.M. Hafizur |
| author_facet | Akhand, M. A. H Ahmed, Mahtab Rahman, M.M. Hafizur |
| author_sort | Akhand, M. A. H |
| building | IIUM Repository |
| collection | Online Access |
| description | Recognition of handwritten numerals has gained much interest in recent years due to its various application
potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use 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. In this study, a CNN based method has been
investigated for Bangla handwritten numeral recognition. A
moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset. |
| first_indexed | 2025-11-14T16:34:01Z |
| format | Proceeding Paper |
| id | iium-53467 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T16:34:01Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-534672019-06-26T07:12:54Z http://irep.iium.edu.my/53467/ Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition Akhand, M. A. H Ahmed, Mahtab 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. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use 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. In this study, a CNN based method has been investigated for Bangla handwritten numeral recognition. A moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset. Institute of Electrical and Electronics Engineers Inc. 2016-12-01 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/53467/7/53467.pdf application/pdf en http://irep.iium.edu.my/53467/13/53467_Convolutional%20neural%20network%20training%20with%20artificial%20pattern_SCOPUS%202016.pdf application/pdf en http://irep.iium.edu.my/53467/19/53467_Convolutional%20neural%20network%20training_WOS.pdf Akhand, M. A. H and Ahmed, Mahtab and Rahman, M.M. Hafizur (2016) Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition. In: 5th International Conference on Informatics, Electronics and Vision (ICIEV), 13th-14th May 2016, Dhaka, Bangladesh. http://ieeexplore.ieee.org/document/7760077/ 10.1109/ICIEV.2016.7760077 |
| spellingShingle | TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Akhand, M. A. H Ahmed, Mahtab Rahman, M.M. Hafizur Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title | Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title_full | Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title_fullStr | Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title_full_unstemmed | Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title_short | Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition |
| title_sort | convolutional neural network training with artificial pattern for bangla handwritten numeral recognition |
| topic | TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| url | http://irep.iium.edu.my/53467/ http://irep.iium.edu.my/53467/ http://irep.iium.edu.my/53467/ http://irep.iium.edu.my/53467/7/53467.pdf http://irep.iium.edu.my/53467/13/53467_Convolutional%20neural%20network%20training%20with%20artificial%20pattern_SCOPUS%202016.pdf http://irep.iium.edu.my/53467/19/53467_Convolutional%20neural%20network%20training_WOS.pdf |