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复杂背景下基于HSV 空间和模板匹配的车牌识别方法研究

  

  • 出版日期:2014-08-30 发布日期:2015-05-05

License Plate Recognition in Complex Background Base on#br# HSV Space and Template Matching

  • Online:2014-08-30 Published:2015-05-05

摘要: 车牌识别技术作为交通管理自动化的重要手段,在交通监视和控制中占有很重要
的地位。车牌识别过程可分为车牌定位、车牌校正、字符分割和字符识别四个部分。在车牌定
位中,若单纯采用纹理特征或颜色特征来进行定位,往往适用于背景较为简单的场景,对复杂
背景的定位效果尚有待改进。在字符分割中,目前单行车牌的分割已比较成熟,但双行车牌的
分割仍不理想。提出一种在HSV 空间下两次颜色标定和纹理特征相结合的定位方法和一种单双
行车牌的字符分割方法。该定位方法利用车牌固定颜色搭配特性,对图片两次标记并利用投影
法定位车牌,对200 张不同背景图片测试,定位准确率达到98%。在字符分割部分,利用改进
的模板匹配方法对字符分割,可适用于单、双行车牌分割,准确率达到95%。

关键词: 车牌定位, 字符分割, 字符识别, SVM 分类器

Abstract: As an important means of the automatic traffic management, the technology of license
plate recognition plays a very important role in transportation surveillance and control. License plate
recognition can generally be divided into four parts: vehicle license plate location, license plate
correction, license plate character segmentation and character recognition. In license plate location, it
is often applied to simple background scene, but not suitable for complex background if only using
the texture or color features of the location. In character segmentation, the method of single line is
mature, but the method of double line license is not ideal. A location method is provided that combine
twice color calibration in HSV space and texture feature and a segmentation method of single or
double rows of characters. Because of the property of fixed colors in a plate, the picture is marked
twice, and then the projection method is used to locate the plates. The experimental result reaches
98% accuracy in the test of 200 different background pictures. Meanwhile, the improved method of
template matching reaches 95% accuracy.

Key words: license plate location, character segmentation, character recognition, SVM classifier