Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network
Handwritten Character recognition falls under the domain of image classification that has been under research for years. The idea is to make the machine recognize handwritten human characters. The language focused in this research paper is English while using offline handwritten character recogniti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sukkur IBA University
2024-10-01
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| Series: | Sukkur IBA Journal of Computing and Mathematical Sciences |
| Online Access: | https://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjcms/article/view/1374 |
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| author | Muhammad Iqbal Muniba Humayun Raheel Siddiqi Christopher J. Harrison Muneeb Abid Malik |
| author_facet | Muhammad Iqbal Muniba Humayun Raheel Siddiqi Christopher J. Harrison Muneeb Abid Malik |
| author_sort | Muhammad Iqbal |
| collection | DOAJ |
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Handwritten Character recognition falls under the domain of image classification that has been under research for years. The idea is to make the machine recognize handwritten human characters. The language focused in this research paper is English while using offline handwritten character recognition for identifying English characters. There are many publically available datasets from which EMNIST is the most challenging one. The main idea of this research paper is to propose a deep learning CNN method to help recognize English characters. This research paper proposes a deep learning convolutional neural network that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyper-parametric settings were used for all the models under test and E-Character with the same data augmentation settings. The proposed model named the E-Character recognizer was able to produce 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some of the problems like misclassification due to the similar structure of characters.
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| format | Article |
| id | doaj-art-ed82db3c99c9478e810f6e10bbdef3db |
| institution | Kabale University |
| issn | 2520-0755 2522-3003 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Sukkur IBA University |
| record_format | Article |
| series | Sukkur IBA Journal of Computing and Mathematical Sciences |
| spelling | doaj-art-ed82db3c99c9478e810f6e10bbdef3db2025-02-11T19:23:47ZengSukkur IBA UniversitySukkur IBA Journal of Computing and Mathematical Sciences2520-07552522-30032024-10-018110.30537/sjcms.v8i1.1374Offline English Handwritten Character Recognition using Sequential Convolutional Neural NetworkMuhammad Iqbal0Muniba HumayunRaheel SiddiqiChristopher J. HarrisonMuneeb Abid Malik 1Bahria University, Karachi2 Department of Civil Engineering, College of Engineering and Technology, University of Sargodha, Pakistan. Handwritten Character recognition falls under the domain of image classification that has been under research for years. The idea is to make the machine recognize handwritten human characters. The language focused in this research paper is English while using offline handwritten character recognition for identifying English characters. There are many publically available datasets from which EMNIST is the most challenging one. The main idea of this research paper is to propose a deep learning CNN method to help recognize English characters. This research paper proposes a deep learning convolutional neural network that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyper-parametric settings were used for all the models under test and E-Character with the same data augmentation settings. The proposed model named the E-Character recognizer was able to produce 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some of the problems like misclassification due to the similar structure of characters. https://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjcms/article/view/1374 |
| spellingShingle | Muhammad Iqbal Muniba Humayun Raheel Siddiqi Christopher J. Harrison Muneeb Abid Malik Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network Sukkur IBA Journal of Computing and Mathematical Sciences |
| title | Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network |
| title_full | Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network |
| title_fullStr | Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network |
| title_full_unstemmed | Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network |
| title_short | Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network |
| title_sort | offline english handwritten character recognition using sequential convolutional neural network |
| url | https://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjcms/article/view/1374 |
| work_keys_str_mv | AT muhammadiqbal offlineenglishhandwrittencharacterrecognitionusingsequentialconvolutionalneuralnetwork AT munibahumayun offlineenglishhandwrittencharacterrecognitionusingsequentialconvolutionalneuralnetwork AT raheelsiddiqi offlineenglishhandwrittencharacterrecognitionusingsequentialconvolutionalneuralnetwork AT christopherjharrison offlineenglishhandwrittencharacterrecognitionusingsequentialconvolutionalneuralnetwork AT muneebabidmalik offlineenglishhandwrittencharacterrecognitionusingsequentialconvolutionalneuralnetwork |