动态场景中车牌检测的一种新方法

Chunliang Zhao, Yuanyuan Hao, Shulin Sui, Shujiao Sui
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引用次数: 1

摘要

车牌检测包括车牌定位、字符分割、字符识别。动态场景下的车牌识别率受多种因素的影响。每个过程偏差都可能影响整个系统的识别率,而每个部分的准确率又受到很多因素的影响,为了降低这种误差,我们结合各种算法的优点,提出了一种综合的检测模型。在车牌定位阶段,我们提出HSV空间和形态方法;在分割字符阶段,我们提出了最大相邻字符水平中心距离分割方法;在字符识别阶段,我们选择使用CNN算法。在最后的仿真测试中,30组车牌识别中存在1组误差,准确率较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Method to Detect the License Plate in Dynamic Scene
License plate detection includes license plate positioning, segmentation characters, character recognition. The recognition rate of license plates under dynamic scenes is affected by many factors. Each process deviation may affect the overall system recognition rate, and the accuracy of each part is affected by many factors, in order to reduce this error, we combine the advantages of a variety of algorithms to propose a comprehensive detection model. In the license plate positioning phase, we propose HSV space and morphological methods; in the segmentation character phase, we propose the maximum adjacent character horizontal center distance segmentation method; in the character recognition stage, we choose to use the CNN algorithm. In the final simulation test, there are a set of 1 errors in the 30 groups of license plate recognition, the accuracy is higher.
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