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All Journal International Journal of Electrical and Computer Engineering
Sinan Q. Salih
Imam Ja’afar Al-Sadiq University
Computer Science & IT Electrical & Electronics Engineering

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An optimized deep learning model for optical character recognition applications Sinan Q. Salih; Ahmed L. Khalaf; Nuha Sami Mohsin; Saadya Fahad Jabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3010-3018

Abstract

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recognition applications; the proposed method was evaluated for performance in terms of computational accuracy, convergence analysis, and cost.
Co-Authors Ahmed L. Khalaf Nuha Sami Mohsin Saadya Fahad Jabbar