Offline Arabic handwritten word recognition: A transfer learning approach
Offline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to co...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821003323 |
| _version_ | 1811197430377480192 |
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| author | Mohamed Awni Mahmoud I. Khalil Hazem M. Abbas |
| author_facet | Mohamed Awni Mahmoud I. Khalil Hazem M. Abbas |
| author_sort | Mohamed Awni |
| collection | DOAJ |
| description | Offline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to compensate for the lack of training samples, but there is a wide controversy about the effectiveness of applying it to cross-domain tasks. In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. Then, we evaluate the performance of the ResNet18 model that has been pre-trained on the ImageNet dataset for the same task. Finally, we propose an approach based on sequentially transferring the mid-level word image representations through two consecutive phases using the ResNet18 model. We carried out four different sets of experiments using two popular offline Arabic handwritten word datasets: the AlexU-W and the IFN/ENIT (v2.0p1e) to figure out the most effective way of applying transfer learning. Our results demonstrate that using the ImageNet as a source dataset improves the recognition accuracy of the ten frequently misclassified words in the IFN/ENIT dataset by 14%, while our proposed approach gives a rise of 35.45%. In the whole dataset, we achieved recognition accuracy up to 96.11%, which is nearly a 2.5% enhancement compared with other state-of-the-art methods. |
| first_indexed | 2024-04-12T01:14:41Z |
| format | Article |
| id | doaj.art-a6fae292c51243159cbfd4d65e4c59e0 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-12T01:14:41Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-a6fae292c51243159cbfd4d65e4c59e02022-12-22T03:54:01ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-11-01341096549661Offline Arabic handwritten word recognition: A transfer learning approachMohamed Awni0Mahmoud I. Khalil1Hazem M. Abbas2Electrical and Computer Engineering Department, Higher Technological Institute, 10th of Ramadan City, Egypt; Corresponding author.Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, EgyptComputer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, EgyptOffline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to compensate for the lack of training samples, but there is a wide controversy about the effectiveness of applying it to cross-domain tasks. In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. Then, we evaluate the performance of the ResNet18 model that has been pre-trained on the ImageNet dataset for the same task. Finally, we propose an approach based on sequentially transferring the mid-level word image representations through two consecutive phases using the ResNet18 model. We carried out four different sets of experiments using two popular offline Arabic handwritten word datasets: the AlexU-W and the IFN/ENIT (v2.0p1e) to figure out the most effective way of applying transfer learning. Our results demonstrate that using the ImageNet as a source dataset improves the recognition accuracy of the ten frequently misclassified words in the IFN/ENIT dataset by 14%, while our proposed approach gives a rise of 35.45%. In the whole dataset, we achieved recognition accuracy up to 96.11%, which is nearly a 2.5% enhancement compared with other state-of-the-art methods.http://www.sciencedirect.com/science/article/pii/S1319157821003323Deep convolutional neural networksOffline Arabic handwritten wordsTransfer learningProgressive resizingResNet-18 |
| spellingShingle | Mohamed Awni Mahmoud I. Khalil Hazem M. Abbas Offline Arabic handwritten word recognition: A transfer learning approach Journal of King Saud University: Computer and Information Sciences Deep convolutional neural networks Offline Arabic handwritten words Transfer learning Progressive resizing ResNet-18 |
| title | Offline Arabic handwritten word recognition: A transfer learning approach |
| title_full | Offline Arabic handwritten word recognition: A transfer learning approach |
| title_fullStr | Offline Arabic handwritten word recognition: A transfer learning approach |
| title_full_unstemmed | Offline Arabic handwritten word recognition: A transfer learning approach |
| title_short | Offline Arabic handwritten word recognition: A transfer learning approach |
| title_sort | offline arabic handwritten word recognition a transfer learning approach |
| topic | Deep convolutional neural networks Offline Arabic handwritten words Transfer learning Progressive resizing ResNet-18 |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821003323 |
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