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Dr. mohamed loey ramadan :: Publications:

CNN for Handwritten Arabic digits Recognition based on LeNet-5
Authors: Ahmed El-Sawy, Hazem EL-Bakry, Mohamed Loey
Year: 2016
Keywords: Not Available
Journal: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016
Volume: 533
Issue: Not Available
Pages: 566-575
Publisher: Springer International Publishing
Local/International: International
Paper Link:
Full paper mohamed loae ramadan_CNN for Handwritten Arabic Digits Recognition Based on LeNet-5.pdf
Supplementary materials Not Available

In recent years, handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. The paper provided a deep learning technique that can be effectively apply to recognizing Arabic handwritten digits. LeNet-5, a Convolutional Neural Network (CNN) trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images. A comparison is held amongst the results, and it is shown by the end that the use of CNN was leaded to significant improvements across different machine-learning classification algorithms.

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