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CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

  • Ahmed El-Sawy
  • Hazem EL-Bakry
  • Mohamed LoeyEmail author
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 533)

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.

Keywords

Recognition Rate Deep Learning Convolutional Neural Network Handwritten Digit Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer and Informatics, Computer Science DepartmentBenha UniversityBenhaEgypt
  2. 2.Faculty of Computer and Information Sciences, Information System DepartmentMansoura UniversityMansouraEgypt

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