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Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Reya Sharma1, Baijnath Kaushik1, Naveen Kumar Gondhi1, Muhammad Tahir2,*, Mohammad Khalid Imam Rahmani2

1 School of Computer Science and Engineering, SMVDU, J&K, India
2 College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

* Corresponding Author: Muhammad Tahir. Email: email

Computers, Materials & Continua 2022, 71(3), 5855-5873.


Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.


Cite This Article

R. Sharma, B. Kaushik, N. Kumar Gondhi, M. Tahir and M. Khalid Imam Rahmani, "Quantum particle swarm optimization based convolutional neural network for handwritten script recognition," Computers, Materials & Continua, vol. 71, no.3, pp. 5855–5873, 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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