Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition
Recognition of handwritten numerals has gained much interest in recent years due to its various potential applications. Bengali is the fifth ranked among the spoken languages of the world. However, due to inherent difficulties of Bengali numeral recognition, a very few study on handwritten Bengali n...
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Modern Education and Computer Science Press
2016
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| Online Access: | http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/1/IJIGSP-V8-N9-6.pdf |
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iium-520282016-09-30T01:52:08Z http://irep.iium.edu.my/52028/ Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition Akhand, M. A. H Ahmed, Mahtab Rahman, M.M. Hafizur TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Recognition of handwritten numerals has gained much interest in recent years due to its various potential applications. Bengali is the fifth ranked among the spoken languages of the world. However, due to inherent difficulties of Bengali numeral recognition, a very few study on handwritten Bengali numeral recognition is found with respect to other major languages. The existing Bengali numeral recognition methods used distinct feature extraction techniques and various classification tools. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this paper, we have investigated a CNN based Bengali handwritten numeral recognition scheme. Since English numerals are frequently used with Bengali numerals, handwritten Bengali-English mixed numerals are also investigated in this study. The proposed scheme uses moderate pre-processing technique to generate patterns from images of handwritten numerals and then employs CNN to classify individual numerals. It does not employ any feature extraction method like other related works. The proposed method showed satisfactory recognition accuracy on the benchmark data set and outperformed other prominent existing methods for both Bengali and Bengali-English mixed cases. Modern Education and Computer Science Press 2016-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/52028/1/IJIGSP-V8-N9-6.pdf Akhand, M. A. H and Ahmed, Mahtab and Rahman, M.M. Hafizur (2016) Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition. International Journal of Image, Graphics and Signal Processing (IJIGSP), 8 (9). pp. 40-50. ISSN 2074-9074 E-ISSN 2074-9082 http://www.mecs-press.org/ijigsp/ijigsp-v8-n9/IJIGSP-V8-N9-6.pdf 10.5815/ijigsp.2016.09.06 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
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Online Access |
| language |
English |
| topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering |
| spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Akhand, M. A. H Ahmed, Mahtab Rahman, M.M. Hafizur Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| description |
Recognition of handwritten numerals has gained much interest in recent years due to its various potential applications. Bengali is the fifth ranked among the spoken languages of the world. However, due to inherent difficulties of Bengali numeral recognition, a very few study on handwritten Bengali numeral recognition is found with respect to other major languages. The existing Bengali numeral recognition methods used distinct feature extraction techniques and various classification tools. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this paper, we have investigated a CNN based Bengali handwritten numeral recognition scheme. Since English numerals are frequently used with Bengali numerals, handwritten Bengali-English mixed numerals are also investigated in this study. The proposed scheme uses moderate pre-processing technique to generate patterns from images of handwritten numerals and then employs CNN to classify individual numerals. It does not employ any feature extraction method like other related works. The proposed method showed satisfactory recognition accuracy on the benchmark data set and outperformed other prominent existing methods for both Bengali and Bengali-English mixed cases. |
| format |
Article |
| 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 |
| title |
Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| title_short |
Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| title_full |
Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| title_fullStr |
Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| title_full_unstemmed |
Convolutional neural network based handwritten Bengali and Bengali-English mixed numeral recognition |
| title_sort |
convolutional neural network based handwritten bengali and bengali-english mixed numeral recognition |
| publisher |
Modern Education and Computer Science Press |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/ http://irep.iium.edu.my/52028/1/IJIGSP-V8-N9-6.pdf |
| first_indexed |
2023-09-18T21:13:46Z |
| last_indexed |
2023-09-18T21:13:46Z |
| _version_ |
1777411425698840576 |