| Autor: |
Ahmad T. Al- Taani, Sadeem T. Ahmad |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Jordanian Journal of Computers and Information Technology, Vol 7, Iss 2, Pp 192-205 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2413-9351 |
| DOI: |
10.5455/jjcit.71-1615204606 |
| Popis: |
This study proposes the use of Residual Neural Networks (ResNets) to recognise Arabic offline isolated handwritten characters including Arabic digits. ResNets is a deep learning approach which showed effectiveness in many applications more than conventional machine learning approaches. The proposed approach consists of three main phases: pre-processing phase, training the ResNet on the training set, and testing the trained ResNet on the datasets. The evaluation of the proposed approach is performed on three available datasets: MADBase, AIA9K, and AHCD. The proposed approach achieved accuracies of 99.8%, 99.05% and 99.55% on these datasets, respectively. It also achieved a validation accuracy of 98.9% on the constructed dataset based on the three datasets. [JJCIT 2021; 7(2.000): 192-205] |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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