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.

中文翻译：

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车牌字符识别的弦长形状特征

尽管在识别性，紧凑性和鲁棒性方面具有公认的优点，但弦长形状特征在车牌识别的文献中并没有引起很多关注。在本文中，我们提出了一种新颖的k最近邻（kNN）方法，用于车牌检测和识别，其中，新的低维描述符结合了车牌字符的形状信息，是由一组有限的一维弦长签名形成的。当在包含相对较大且多样化的印版图像数据集合的数据集上进行评估时，所提出的方法可提供与文献报道的结果相比有希望的结果，而不会牺牲计算效率或稳定性。