Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 19, 08 December 2023
Open
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License plate Chinese character recognition based on ViT model
* Author to whom correspondence should be addressed.
Abstract
Transformer applications have been widely used in the computer vision field. Many related literatures show that the advantages of the model such as increased receptive field and globality are gradually emerging in image processing. However, with the popularity of the transformer, whether it can compete with the convolutional neural network (CNN) in terms of performance is still questionable and remains to be further studied. This paper will use the most basic structural model in the visual transformer (ViT) to classify and identify Chinese characters that are frequently used in the field of transportation and logistics and compare them with two classical CNN models. The results demonstrate that the performance of the transformer is obviously better than that of the traditional CNN structure, and the final accuracy of character recognition is higher than that of CNN, up to 98.66 %. It fully shows the infinite potential and excellent performance of the transformer in the area of computer vision and has high reliability and generalization ability.
Keywords
Chinese characters, vision transformer, convolutional neural network.
References
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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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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-203-9
- ISBN (Online)
- 978-1-83558-204-6
- Published Date
- 08 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/19/20230458
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited