Abstract
Despite their recognized merits in terms of discrimination, compactness, and robustness, chord-length shape features have not received a great deal of attention in the literature on license plate recognition. In this paper, we present an innovative k nearest neighbors (kNN) approach for license plate detection and recognition, where a new low-dimensional descriptor that incorporates shape information of plate characters is formed from a finite set of established 1D chord-length signatures. When evaluated on a dataset incorporating a relatively large and diverse collection of plate image data, the proposed approach delivers promising results that compare favorably with those reported in the literature, without sacrificing computational efficiency or stability.
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Bakheet, S., Al-Hamadi, A. Chord-Length Shape Features for License Plate Character Recognition. J Russ Laser Res 41, 156–170 (2020). https://doi.org/10.1007/s10946-020-09861-1
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DOI: https://doi.org/10.1007/s10946-020-09861-1