Recognition of Arabic handwritten characters using residual neural networks

المؤلفون المشاركون

al-Taani, Ahmad T.
Ahmad, Sadim T.

المصدر

Jordanian Journal of Computetrs and Information Technology

العدد

المجلد 7، العدد 2 (30 يونيو/حزيران 2021)، ص ص. 192-205، 14ص.

الناشر

جامعة الأميرة سمية للتكنولوجيا

تاريخ النشر

2021-06-30

دولة النشر

الأردن

عدد الصفحات

14

التخصصات الرئيسية

العلوم التربوية
تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

This study proposes the use of Residual Neural Networks (ResNets) to recognize 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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

al-Taani, Ahmad T.& Ahmad, Sadim T.. 2021. Recognition of Arabic handwritten characters using residual neural networks. Jordanian Journal of Computetrs and Information Technology،Vol. 7, no. 2, pp.192-205.
https://search.emarefa.net/detail/BIM-1415801

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

al-Taani, Ahmad T.& Ahmad, Sadim T.. Recognition of Arabic handwritten characters using residual neural networks. Jordanian Journal of Computetrs and Information Technology Vol. 7, no. 2 (Jun. 2021), pp.192-205.
https://search.emarefa.net/detail/BIM-1415801

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

al-Taani, Ahmad T.& Ahmad, Sadim T.. Recognition of Arabic handwritten characters using residual neural networks. Jordanian Journal of Computetrs and Information Technology. 2021. Vol. 7, no. 2, pp.192-205.
https://search.emarefa.net/detail/BIM-1415801

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references : p. 204-205

رقم السجل

BIM-1415801