Convolutional neural network-based ensemble methods to recognize Bangla handwritten character
In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-06-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-565.pdf |
| _version_ | 1818889884822142976 |
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| author | Mir Moynuddin Ahmed Shibly Tahmina Akter Tisha Tanzina Akter Tani Shamim Ripon |
| author_facet | Mir Moynuddin Ahmed Shibly Tahmina Akter Tisha Tanzina Akter Tani Shamim Ripon |
| author_sort | Mir Moynuddin Ahmed Shibly |
| collection | DOAJ |
| description | In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated. |
| first_indexed | 2024-12-19T17:16:07Z |
| format | Article |
| id | doaj.art-23e965a473ae45a3b3d3b39cc493e9f7 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-19T17:16:07Z |
| publishDate | 2021-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-23e965a473ae45a3b3d3b39cc493e9f72022-12-21T20:12:52ZengPeerJ Inc.PeerJ Computer Science2376-59922021-06-017e56510.7717/peerj-cs.565Convolutional neural network-based ensemble methods to recognize Bangla handwritten characterMir Moynuddin Ahmed ShiblyTahmina Akter TishaTanzina Akter TaniShamim RiponIn this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.https://peerj.com/articles/cs-565.pdfConvolutional neural networkEnsemble learningBangla handwritten character recognitionDeep learningStacked generalizationBootstrap aggregating |
| spellingShingle | Mir Moynuddin Ahmed Shibly Tahmina Akter Tisha Tanzina Akter Tani Shamim Ripon Convolutional neural network-based ensemble methods to recognize Bangla handwritten character PeerJ Computer Science Convolutional neural network Ensemble learning Bangla handwritten character recognition Deep learning Stacked generalization Bootstrap aggregating |
| title | Convolutional neural network-based ensemble methods to recognize Bangla handwritten character |
| title_full | Convolutional neural network-based ensemble methods to recognize Bangla handwritten character |
| title_fullStr | Convolutional neural network-based ensemble methods to recognize Bangla handwritten character |
| title_full_unstemmed | Convolutional neural network-based ensemble methods to recognize Bangla handwritten character |
| title_short | Convolutional neural network-based ensemble methods to recognize Bangla handwritten character |
| title_sort | convolutional neural network based ensemble methods to recognize bangla handwritten character |
| topic | Convolutional neural network Ensemble learning Bangla handwritten character recognition Deep learning Stacked generalization Bootstrap aggregating |
| url | https://peerj.com/articles/cs-565.pdf |
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