Chord-Length Shape Features for License Plate Character Recognition

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.