[Paper] Discriminating Car License Plate Numbers on Low Resolution Using Sparse PCA

License plates captured in surveillance videos often have insufficient resolution, making it difficult to recognize the numbers. In this paper, we propose a novel method for license plate number recognition using sparse PCA coefficients and Naive Bayes classifier. The proposed method is applied to low-resolution license plate images, and its performance is compared with two conventional methods: moment features with Bayes classifier and PCA coefficients with naive Bayes classifier. The evaluation results, including the first-candidate recognition rate, the recognition rate up to the second candidate, and the classification cross-entropy, show that the proposed method achieves the best performance.

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Publication Details

Journal
ITE Transactions on Media Technology and Applications
Published
2024-12-31
DOI
https://doi.org/10.3169/mta.13.119
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
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article

[Paper] Discriminating Car License Plate Numbers on Low Resolution Using Sparse PCA

Koji Shinomiya, Kazuhiro Fujita, Ryotaro Ooe
ITE Transactions on Media Technology and Applications
Vehicle License Plate Recognition
article

[Paper] Discriminating Car License Plate Numbers on Low Resolution Using Sparse PCA

Koji Shinomiya, Kazuhiro Fujita, Ryotaro Ooe
article en

Abstract

License plates captured in surveillance videos often have insufficient resolution, making it difficult to recognize the numbers. In this paper, we propose a novel method for license plate number recognition using sparse PCA coefficients and Naive Bayes classifier. The proposed method is applied to low-resolution license plate images, and its performance is compared with two conventional methods: moment features with Bayes classifier and PCA coefficients with naive Bayes classifier. The evaluation results, including the first-candidate recognition rate, the recognition rate up to the second candidate, and the classification cross-entropy, show that the proposed method achieves the best performance.

ITE Transactions on Media Technology and ApplicationsVol. 13(1)
Ryukoku University (JP), Hyogo Prefectural Police (JP)
Reduced inequalities
Openalex Percentile: Top 63%
Vehicle License Plate Recognition
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