A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN)
<p dir="ltr">Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarch...
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2024
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| _version_ | 1864513548227444736 |
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| author | Muniba Humayun (21323750) |
| author2 | Raheel Siddiqi (19923944) Mueen Uddin (4903510) Irfan Ali Kandhro (17541876) Maha Abdelhaq (735574) Raed Alsaqour (735575) |
| author2_role | author author author author author |
| author_facet | Muniba Humayun (21323750) Raheel Siddiqi (19923944) Mueen Uddin (4903510) Irfan Ali Kandhro (17541876) Maha Abdelhaq (735574) Raed Alsaqour (735575) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muniba Humayun (21323750) Raheel Siddiqi (19923944) Mueen Uddin (4903510) Irfan Ali Kandhro (17541876) Maha Abdelhaq (735574) Raed Alsaqour (735575) |
| dc.date.none.fl_str_mv | 2024-08-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-024-10206-1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_methodology_for_offline_English_handwritten_character_recognition_using_ELBP-based_sequential_CNN_/29022167 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning ELBP Handwritten recognition Character recognition Deep learning Offline handwritten CNN |
| dc.title.none.fl_str_mv | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), 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 hyperparametric 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, produced 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 problems, like misclassification due to the similar structure of characters.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10206-1" target="_blank">https://dx.doi.org/10.1007/s00521-024-10206-1</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_494bb6360d4ec90ed492672588de2ac9 |
| identifier_str_mv | 10.1007/s00521-024-10206-1 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29022167 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN)Muniba Humayun (21323750)Raheel Siddiqi (19923944)Mueen Uddin (4903510)Irfan Ali Kandhro (17541876)Maha Abdelhaq (735574)Raed Alsaqour (735575)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningELBPHandwritten recognitionCharacter recognitionDeep learningOffline handwrittenCNN<p dir="ltr">Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), 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 hyperparametric 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, produced 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 problems, like misclassification due to the similar structure of characters.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10206-1" target="_blank">https://dx.doi.org/10.1007/s00521-024-10206-1</a></p>2024-08-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-024-10206-1https://figshare.com/articles/journal_contribution/A_novel_methodology_for_offline_English_handwritten_character_recognition_using_ELBP-based_sequential_CNN_/29022167CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290221672024-08-02T03:00:00Z |
| spellingShingle | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) Muniba Humayun (21323750) Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning ELBP Handwritten recognition Character recognition Deep learning Offline handwritten CNN |
| status_str | publishedVersion |
| title | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| title_full | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| title_fullStr | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| title_full_unstemmed | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| title_short | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| title_sort | A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN) |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning ELBP Handwritten recognition Character recognition Deep learning Offline handwritten CNN |