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CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 533)


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.


  • Recognition Rate
  • Deep Learning
  • Convolutional Neural Network
  • Handwritten Digit
  • Digit Recognition

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Correspondence to Mohamed Loey .

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El-Sawy, A., EL-Bakry, H., Loey, M. (2017). CNN for Handwritten Arabic Digits Recognition Based on LeNet-5. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham.

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