Recognize Arabic handwritten using CNN model
Joint Authors
al-Khalid, Farah F.
Ulaywi, Bushra Kazim
M., Abd al-Muhsin
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 27, Issue 6 (31 Dec. 2019), pp.359-367, 9 p.
Publisher
Publication Date
2019-12-31
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Abstract EN
One of the most challenges that face machine learning is handwritten recognition, especially Arabic scripts, because many styles found for Arabic font.
in this paper, an investigation model is proposed to make recognition for Arabic handwritten scripts utilizing convolutional neural network (CNN), with multi layers of normalization and regularization to reduce training time and increase overall accuracy, with validation accuracy 98% for kaggle dataset for Arabic handwritten characters and digits using python.
American Psychological Association (APA)
M., Abd al-Muhsin& Ulaywi, Bushra Kazim& al-Khalid, Farah F.. 2019. Recognize Arabic handwritten using CNN model. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 27, no. 6, pp.359-367.
https://search.emarefa.net/detail/BIM-1316602
Modern Language Association (MLA)
al-Khalid, Farah F.…[et al.]. Recognize Arabic handwritten using CNN model. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 27, no. 6 (2019), pp.359-367.
https://search.emarefa.net/detail/BIM-1316602
American Medical Association (AMA)
M., Abd al-Muhsin& Ulaywi, Bushra Kazim& al-Khalid, Farah F.. Recognize Arabic handwritten using CNN model. Journal of Babylon University : Journal of Applied and Pure Sciences. 2019. Vol. 27, no. 6, pp.359-367.
https://search.emarefa.net/detail/BIM-1316602
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 366-367
Record ID
BIM-1316602