Deep Convolutional Neural Networks Based on Knowledge Distillation for Offline Handwritten Chinese Character Recognition-Reference-Cited by-同舟云学术

Deep Convolutional Neural Networks Based on Knowledge Distillation for Offline Handwritten Chinese Character Recognition

Published:2024-03-20 Issue:2 Volume:28 Page:231-238
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

He Hongli¹^ORCID,Zhu Zongnan²^ORCID,Li Zhuo³^ORCID,Dan Yongping²^ORCID

Affiliation:

1. Rail Transit Institute, Henan College of Transportation, No.259 Tonghui Road, Baisha Vocational Education Park, Zhengdong New District, Zhengzhou 450061, China

2. School of Electronic and Information, Zhongyuan University of Technology, No.41 Zhongyuan Road, Zhengzhou 450007, China

3. Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

Abstract

Deep convolutional neural networks (DNNs) have achieved outstanding performance in this field. Meanwhile, handwritten Chinese character recognition (HCCR) is a challenging area of research in the field of computer vision. DNNs require a large number of parameters and high memory consumption. To address these issues, this paper proposes an approach based on an attention mechanism and knowledge distillation. The attention mechanism improves the feature extraction and the knowledge distillation reduces the number of parameters. The experimental results show that ResNet18 achieves a recognition accuracy of 97.63% on the HCCR dataset with 11.25 million parameters. Compared with other methods, this study improves the performance for HCCR.

Publisher

Fuji Technology Press Ltd.

Reference40 articles.

1. C.-L. Liu, “High Accuracy Handwritten Chinese Character Recognition Using Quadratic Classifiers with Discriminative Feature Extraction,” 18th Conf. on Pattern Recognition (ICPR’06), Vol.2, pp. 942-945, 2006. https://doi.org/10.1109/ICPR.2006.624

2. T. H. Hildebrandt and W. Liu, “Optical Recognition of Handwritten Chinese Characters: Advances Since 1980,” Pattern Recognition, Vol.26, pp. 205-225, 1993. https://doi.org/10.1016/0031-3203(93)90030-Z

3. X. Zhang, Y. Bengio, and C. Liu, “Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark,” Pattern Recognition, Vol.61, pp. 348-360, 2016. https://doi.org/10.1016/j.patcog.2016.08.005

4. R. Dai, C. Liu, and B. Xiao, “Chinese Character Recognition: History, Status and Prospects,” Frontiers of Computer Science in China, Vol.1, No.2, pp. 126-136, 2007. https://doi.org/10.1007/s11704-007-0012-5

5. C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang, “Online and Offline Handwritten Chinese Character Recognition: Benchmarking on New Databases,” Pattern Recognition, Vol.46, No.1, pp. 155-162, 2013. https://doi.org/10.1016/j.patcog.2012.06.021