Network architecture exploration for Chinese character recognition with densenet

Research Article
Open access

Network architecture exploration for Chinese character recognition with densenet

Ruilin Dai 1*
  • 1 Jinan University    
  • *corresponding author drl20096@163.con
Published on 8 December 2023 | https://doi.org/10.54254/2753-8818/18/20230456
TNS Vol.18
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-201-5
ISBN (Online): 978-1-83558-202-2

Abstract

Chinese character recognition can be widely used in many fields. Though Resnet and Densenet are both used in this area already, using these two networks and making a comparison on training performances between them is a ground that has not been explored. In this paper, these two methods are built and compared. Firstly, a dataset of Chinese character including 5,772 images with 28*28 size will be introduced. Next, Resnet and Densenet model pre-trained on the dataset is selected. Then fine-tuning is done to improve the accuracy of networks. After 50 epochs of training, the final result shows that Densenet is more stable compared with Resnet but less efficient with more epoches to perform well.

Keywords:

convolutional neural network, DenseNet, ResNet, Chinese character recognition

Dai,R. (2023). Network architecture exploration for Chinese character recognition with densenet. Theoretical and Natural Science,18,308-314.
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References

[1]. Sun, Jinhu Li, Peng Wu, Xiaojun, Handwritten Ancient Chinese Character Recognition Algorithm Based on Improved Inception-ResNet and Attention Mechanism, 2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022, 2022

[2]. Huang, Zetao Zhang, Qian ,Skew correction of handwritten Chinese character based on resnet, 2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019, 2019

[3]. Patnaik, Suprava Kumari, Saloni Das Mahapatra, Shreya , Comparison of deep CNN and ResNet for Handwritten Devanagari Character Recognition, 1st IEEE International Conference for Convergence in Engineering, ICCE 2020, KIIT and School of Electronics Bhubaneswar India, 2020

[4]. Khan, Riaz Ullah Zhang, Xiaosong Kumar, Rajesh , Analysis of ResNet and GoogleNet models for malware detection, JOURNAL IN COMPUTER VIROLOGY, Volume 15, Issue 1,2019, p29-p37

[5]. Lu, Huimin Yang, Rui Deng, Zhenrong Zhang, Yonglin Gao, Guangwei Lan, Rushi, Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM, ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS AND APPLICATIONS, Volume 17, Number 1, 2021, p1-p18

[6]. Jalali, Amin Lee, Minho , High cursive traditional Asian character recognition using integrated adaptive constraints in ensemble of DenseNet and Inception models, PATTERN RECOGNITION LETTERS, Volume 131, as of 2020, p172-p177

[7]. Miao, Jun Xu, Shaowu Zou, Baixian Qiao, Yuanhua ResNet based on feature-inspired gating strategy,MULTIMEDIA TOOLS AND APPLICATIONS,Volume 81,Issue 14,2022, p19283-p19300

[8]. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun,Deep Residual Learning for Image Recognition, 10 Dec 2015 , arXiv:1512.03385

[9]. Yu, Dawei Yang, Jie Zhang, Yun Yu, Shujuan Additive DenseNet: Dense connections based on simple addition operations, JOURNAL OF INTELLIGENT AND FUZZY SYSTEMS, Volume 40, Issue 3, 2021, p5015-p5025

[10]. Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger ,Densely Connected Convolutional Networks, 25 Aug 2016, arXiv:1608.06993

Cite this article

Dai,R. (2023). Network architecture exploration for Chinese character recognition with densenet. Theoretical and Natural Science,18,308-314.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics

ISBN：978-1-83558-201-5(Print) / 978-1-83558-202-2(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.18
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Sun, Jinhu Li, Peng Wu, Xiaojun, Handwritten Ancient Chinese Character Recognition Algorithm Based on Improved Inception-ResNet and Attention Mechanism, 2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022, 2022

[2]. Huang, Zetao Zhang, Qian ,Skew correction of handwritten Chinese character based on resnet, 2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019, 2019

[3]. Patnaik, Suprava Kumari, Saloni Das Mahapatra, Shreya , Comparison of deep CNN and ResNet for Handwritten Devanagari Character Recognition, 1st IEEE International Conference for Convergence in Engineering, ICCE 2020, KIIT and School of Electronics Bhubaneswar India, 2020

[4]. Khan, Riaz Ullah Zhang, Xiaosong Kumar, Rajesh , Analysis of ResNet and GoogleNet models for malware detection, JOURNAL IN COMPUTER VIROLOGY, Volume 15, Issue 1,2019, p29-p37

[5]. Lu, Huimin Yang, Rui Deng, Zhenrong Zhang, Yonglin Gao, Guangwei Lan, Rushi, Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM, ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS AND APPLICATIONS, Volume 17, Number 1, 2021, p1-p18

[6]. Jalali, Amin Lee, Minho , High cursive traditional Asian character recognition using integrated adaptive constraints in ensemble of DenseNet and Inception models, PATTERN RECOGNITION LETTERS, Volume 131, as of 2020, p172-p177

[7]. Miao, Jun Xu, Shaowu Zou, Baixian Qiao, Yuanhua ResNet based on feature-inspired gating strategy,MULTIMEDIA TOOLS AND APPLICATIONS,Volume 81,Issue 14,2022, p19283-p19300

[8]. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun,Deep Residual Learning for Image Recognition, 10 Dec 2015 , arXiv:1512.03385

[9]. Yu, Dawei Yang, Jie Zhang, Yun Yu, Shujuan Additive DenseNet: Dense connections based on simple addition operations, JOURNAL OF INTELLIGENT AND FUZZY SYSTEMS, Volume 40, Issue 3, 2021, p5015-p5025

[10]. Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger ,Densely Connected Convolutional Networks, 25 Aug 2016, arXiv:1608.06993

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