Athlete Number Plate Application Based on Deep Learning Image Recognition
- DOI
- 10.2991/978-94-6463-370-2_37How to use a DOI?
- Keywords
- Deep Learning; Racing Bib Number Recognition; Numberplate Localization
- Abstract
With the increasing popularity of sports competitions, the recognition of number plate images has become a label for athletes in order to make it easier to monitor their progress and status in real time. This paper proposes a comprehensive deep convolutional neural network model for number plate recognition. The model is divided into three modules: the localization of the number plate, the pre-processing of the number plate and the character recognition. Deep learning algorithms were applied to number plate localization for high accuracy, number plate pre-processing using image enhancement techniques, projection methods for character segmentation, and finally a model combining multiple templates and BP neural networks for character recognition. Better results and potential for athlete number plate recognition is provided by the model proposed in this study. Further work is planned to test the robustness of the model in more complex scenarios and to apply it to real-world scenarios to provide more accurate number plate recognition for sporting events.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yongqin Ba AU - Jiawen Sun PY - 2024 DA - 2024/02/14 TI - Athlete Number Plate Application Based on Deep Learning Image Recognition BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 343 EP - 353 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_37 DO - 10.2991/978-94-6463-370-2_37 ID - Ba2024 ER -