Citation

Lim, Jia Min and Lim, Kian Ming and Lee, Chin Poo and Chin, Hui Xin and Hoi, Jin Kang and Pong, Joshua Jing Sheng (2023) A Comparative Study of Learning-based Approaches for Chinese Character Recognition. In: 2023 11th International Conference on Information and Communication Technology (ICoICT), 23-24 August 2023, Melaka, Malaysia.

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Abstract

This paper presents a comprehensive comparison study of various learning-based approaches for Chinese Character Recognition (CCR). The study examines eight types of models that belong to the machine learning model and deep learning model categories. These models include Bagging k-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), Bagging Decision Tree, Xception, LeNet, Multi-Layer Perceptron (MLP), and Visual Geometry Group 16 (VGG16). To conduct the study, a dataset of handwritten Chinese characters is collected. The dataset consists of 5,000 samples distributed across 10 classes of Chinese characters. From the experiment results, we conclude that the best-performing algorithm for the classification model is VGG16, which achieved the highest accuracy score among the eight learning-based models tested in the study. Specifically, VGG16 scored a remarkable accuracy of 99.20%, outperforming the other seven learning-based models. These findings demonstrate the potential of deep learning models, such as VGG16, to improve Chinese character recognition algorithms and enhance their accuracy and performance.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Bagging, VGG16, Chinese Character Recognition, Machine Learning, Deep Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 01:25
Last Modified:	31 Oct 2023 01:25
URII:	http://shdl.mmu.edu.my/id/eprint/11760

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