Visually Similar Handwritten Chinese Character Recognition with Convolutional Neural Network


Liu, Wei Han and Lim, Kian Ming and Lee, Chin Poo (2021) Visually Similar Handwritten Chinese Character Recognition with Convolutional Neural Network. In: 2021 9th International Conference on Information and Communication Technology (ICoICT), 3-5 Aug. 2021, Yogyakarta, Indonesia.

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Computer vision has penetrated many domains, for instance, security, sports, health and medicine, agriculture, transportation, manufacturing, retail, and so like. One of the computer vision tasks is character recognition. In this work, a visually similar handwritten Chinese character dataset is collected. Subsequently, an enhanced convolutional neural network is proposed for the recognition of visually similar handwritten Chinese characters. The convolutional neural network is enhanced by the dropout regularization and early stopping mechanism to reduce the overfitting problem. The Adam optimizer is also leveraged to accelerate and optimize the training process of the convolutional neural network. The empirical results demonstrate that the enhanced convolutional neural network achieves a 97% accuracy, thus corroborate it has better discriminating power in visually similar handwritten Chinese character recognition.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks (Computer science), Chinese character recognition, handwritten Chinese character recognition, convolutional neural network, CNN
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 04 Nov 2021 07:24
Last Modified: 04 Nov 2021 07:24


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