TY - GEN
T1 - A Comparative Study of Learning-based Approaches for Chinese Character Recognition
AU - Lim, Jia Min
AU - Lim, Kian Ming
AU - Lee, Chin Poo
AU - Chin, Hui Xin
AU - Hoi, Jin Kang
AU - Pong, Joshua Jing Sheng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Bagging
KW - Chinese Character Recognition
KW - Deep Learning
KW - Machine Learning
KW - VGG16
UR - http://www.scopus.com/inward/record.url?scp=85174397050&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262670
DO - 10.1109/ICoICT58202.2023.10262670
M3 - Conference contribution
AN - SCOPUS:85174397050
T3 - International Conference on ICT Convergence
SP - 295
EP - 300
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
PB - IEEE Computer Society
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -