In this paper, a novel approach for vehicle license plate detection that improves in both efficiency and quality over the common multiscale search method is proposed. The detection efficiency is improved by employing the result of a single scale sliding window search as a promising guess of the license plate location. The quality is assured by locally refining the initial detection in multiple scales. The main benefit of our method is that we have reached a more precise detection with the analysis of 20 times fewer detection windows with high reliability (96% recall and 70% precision). We also compared our method with an edge-based hybrid approach.
CITATION STYLE
Prates, R. C., Cámara-Chávez, G., Schwartz, W. R., & Menotti, D. (2014). An adaptive vehicle license plate detection at higher matching degree. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8827, pp. 454–461). Springer Verlag. https://doi.org/10.1007/978-3-319-12568-8_56
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