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A sequential handwriting recognition model based on a dynamically configurable CRNN

Al-Saffar, Ahmed and Suryanti, Awang and Al-Saiagh, Wafaa and Al-Khaleefa, Ahmed Salih and Abed, Saad Adnan (2021) A sequential handwriting recognition model based on a dynamically configurable CRNN. Sensors, 21 (21). pp. 1-25. ISSN 1424-8220

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Abstract

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Handwriting recognition; Neural Architecture Search (NAS), configuration search; Meta-heuristics optimization; Deep learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 06 Jan 2022 04:24
Last Modified: 06 Jan 2022 04:24
URI: http://umpir.ump.edu.my/id/eprint/32663
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