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

University of Babylon

Publication Date

2019-12-31

Country of Publication

Iraq

No. of Pages

9

Main Subjects

Educational Sciences

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)

M., Abd al-Muhsin…[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