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

Computers, Materials & Continua 2022年第6期71卷 5855-5873页

作者： reya sharma Baijnath Kaushik Naveen Kumar Gondhi Muhammad Tahir Mohammad Khalid Imam Rahmani School of Computer Science and Engineering SMVDUJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition *** field has gained much interest lately due to its diverse application ***,different methods are available for automatic script *** most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning ***,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them *** approach often requires manual intervention with domain expertise which consumes substantial time and computational *** 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)*** 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 ***,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

关键词： Neuro-evolution quantum particle swarm optimization deep learning convolutional neural networks handwriting recognition

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