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A Novel Multilevel Stacked SqueezeNet Model for Handwritten Chinese Character Recognition

Yuankun Du¹, Fengping Liu² and Zhilong Liu³

1. School of Big Data and Artificial Intelligence, Zhengzhou University of Science and Technology
   450002 Zhengzhou, China
   duyuankk@163.com
2. School of Information Engineering, Zhengzhou University of Science and Technology
   450002 Zhengzhou, China
   liufengppp@163.com
3. Library of Henan Institute of Animal Husbandry and Economics
   450002 Zhengzhou, China
   liuzhilonglo@163.com

Abstract

To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.

Key words

Handwritten Chinese character recognition, multilevel stacked SqueezeNet model, inter-layer feature fusion, L2 norm

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS221210030D

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Du, Y., Liu, F., Liu, Z.: A Novel Multilevel Stacked SqueezeNet Model for Handwritten Chinese Character Recognition. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS221210030D