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Further Investigations for Documents Information Retrieval Based on DWT Read chapter Read first chapter

2017 | OriginalPaper | Chapter

CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

Authors : Ahmed El-Sawy, Hazem EL-Bakry, Mohamed Loey

Published in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Publisher: Springer International Publishing

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Abstract

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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Metadata
Title
CNN for Handwritten Arabic Digits Recognition Based on LeNet-5
Authors
Ahmed El-Sawy
Hazem EL-Bakry
Mohamed Loey
Copyright Year
2017
Book
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Print ISBN: 978-3-319-48307-8

Electronic ISBN: 978-3-319-48308-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-48308-5_54

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