Offline handwritten Chinese character using convolutional neural network: State-of-the-art methods

Zhong, Yingna and Kauthar, Mohd Daud and Ain Najiha, Mohamad Nor and Ikuesan, Richard Adeyemi and Moorthy, Kohbalan (2023) Offline handwritten Chinese character using convolutional neural network: State-of-the-art methods. Journal of Advanced Computational Intelligence and Intelligent Informatics, 27 (4). pp. 567-575. ISSN 1343-0130. (Published)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Activation functions; Convolutional neural network; Filtering techniques; Handwritten Chinese characters recognition
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: College of Engineering
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 21 Aug 2023 03:08
Last Modified: 21 Aug 2023 03:08
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