Towards global recognition: Chinese recognition with non-local attention

Yifan Guo

doi:10.54254/2753-8818/5/20230457

Research Article

Open access

Published on 25 May 2023

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Guo,Y. (2023). Towards global recognition: Chinese recognition with non-local attention. Theoretical and Natural Science,5,691-697.

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Towards global recognition: Chinese recognition with non-local attention

Yifan Guo *^,1,

¹ Dalian University of Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/5/20230457

Abstract

As a part of image recognition, Chinese character recognition has a great application market in China, such as license plate recognition, logistics information recognition and so on. In recent years, the application of CNN has set off a frenzy of computer vision. Especially in the task of image recognition, CNN is widely used because of its high accuracy and few calculation parameters. However, CNN’s local operation of using repeated filters to process images also has its shortcomings, for example, it can’t pay attention to the relationship between distant pixels in the image. To solve this problem, we tried to add non-local operation to CNN to improve its performance. We chose the classic model of ResNetV2-50 as the foundation, and added non-local blocks to it. We compared the results of the two models and found that the accuracy increased by 4%.

Keywords

convolutional neural network, non-local block, Chinese character recognition

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References

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Cite this article

Guo,Y. (2023). Towards global recognition: Chinese recognition with non-local attention. Theoretical and Natural Science,5,691-697.

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 (CONF-CIAP 2023)

Conference website: https://www.confciap.org/

ISBN：978-1-915371-53-9(Print) / 978-1-915371-54-6(Online)

Conference date: 25 March 2023

Editor：Marwan Omar, Roman Bauer

Series: Theoretical and Natural Science

Volume number: Vol.5

ISSN：2753-8818(Print) / 2753-8826(Online)

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