Abstract
Automatic license plate recognition (LPR) is an important functionality for closed-circuit television (CCTV) forensics. However, uncontrolled capture conditions make it still difficult to achieve effective LPR in practice. In this paper, we propose a novel method for robust LPR in real-world imagery, leveraging sparse representation-based (SR-based) super-resolution. To that end, we make use of high-resolution license plate (LP) images that are used for both (1) the construction of a dictionary for SR-based super-resolution and (2) the training of LP character classifiers. Comparative experimental results indicate that the proposed SR-based super-resolution method allows for effective LPR in low-resolution imagery captured by long-distance CCTV cameras.
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Acknowledgment
This work was supported by a grant from the National Research Foundation (NRF) of Korea (grant number: NRF-2012K2A1A2033054).
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Min, Hs., Lee, S.H., De Neve, W., Ro, Y.M. (2014). Improved License Plate Recognition for Low-Resolution CCTV Forensics by Integrating Sparse Representation-Based Super-Resolution. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_32
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DOI: https://doi.org/10.1007/978-3-662-43886-2_32
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