基于特征点和主成分分析的车牌倾斜校正

Wu Guo-ping, Cheng Shi, Ao Min-si, L. Hui
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引用次数: 1

摘要

车牌倾斜校正在车牌自动识别系统中起着重要的作用。为了减少或避免噪声干扰和车牌碎片化对车牌识别的不利影响,并提高计算速度,提出了一种基于特征点和主成分分析(PCA)的车牌识别方法。特征点被认为是车牌上字符的边缘,具有线条的有序性,反映了车牌的倾斜角度。首先对板的特征点进行预处理,通过对特征点的主成分分析得到主成分的方向,即板的倾斜角,然后对板进行校正。实验结果表明,该方法具有简单、对车牌图像质量和帧的要求较低的优点,与霍夫变换等其他方法相比,车牌校正更容易、精度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Slant Correction of Vehicle License Plate Based on Feature Point and Principal Component Analysis
Slant Correction plays an important role in vehicle license plate automatic recognition system. In order to reduce or avoid adverse effects caused by noise disturbance and fragmentary frame of vehicle license plate, as well as speed up the computation, an approach based on feature point and principal component analysis (PCA) is presented. Feature points are considered as the edge of characters on the license plate, with a lined orderliness which reflects the slant angle of the plate. Firstly, a pretreatment process is carried out to extract the feature points of the plate, and the direction of the principal component, which is considered as the slant angle of the plate, is achieved through principal component analysis of the feature points, then the correction of the plate is accomplished. The experimental results demonstrate that, this method enjoys the advantages of being simple and less demanding in image quality and frame of license plate, and makes the correction of the plate easier and more precise compare to some other method such as Hough transform.
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