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JACIII Vol.27 No.4 pp. 567-575
doi: 10.20965/jaciii.2023.p0567
(2023)

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Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Yingna Zhong*, Kauthar Mohd Daud*,† ORCID Icon, Ain Najiha Binti Mohamad Nor*, Richard Adeyemi Ikuesan** ORCID Icon, and Kohbalan Moorthy*** ORCID Icon

*Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
Bangi, Selangor 43600, Malaysia

†Corresponding author

**Department of Computing and Applied Technology, College of Technological Innovation, Zayed University
Abu Dhabi 19282, United Arab Emirates

***Faculty of Computing, Universiti Malaysia Pahang
Pekan, Pahang 26600, Malaysia

Received:
October 19, 2022
Accepted:
March 7, 2023
Published:
July 20, 2023
Creative Commons License
Keywords:
handwritten Chinese characters recognition, convolutional neural network, filtering techniques, activation functions
Abstract

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

Cite this article as:
Y. Zhong, K. Daud, A. Nor, R. Ikuesan, and K. Moorthy, “Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 567-575, 2023.
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