An optimized deep learning model for optical character recognition applications

Sinan Q. Salih, Ahmed L. Khalaf, Nuha Sami Mohsin, Saadya Fahad Jabbar

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.

Keywords


chaotic maps; convolutional neural network; training algorithm; computational intelligence; black hole algorithm;

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v13i3.pp3010-3018

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578