traffic control license plate recognition character recognit

  本文关键词：BP神经网络联合模板匹配的车牌识别系统，由笔耕文化传播整理发布。

BP神经网络联合模板匹配的车牌识别系统

License plate recognition system using a BP neural network and template matching

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GUO Runhua, SU Tingting, MA Xiaowei （Department of Civil Engineering, Tsinghua University, Beijing 100084, China）

清华大学土木工程系,北京100084

文章摘要：车牌识别是智能交通系统的重要部分，对实时自动监控具有重要的意义。用科学的方法识别字符，同时提高识别准确率是改善车牌识别系统的核心问题。由于采集的车牌图像会存在噪音和干扰，，现有方法存在识别效率低下的问题。本文基于Matlab平台，联合应用BP（backpropagation）神经网络和模板匹配方法优化车牌字符识别方法，在神经网络收敛的情况下嵌入模板匹配方法精确识别。针对车牌的特点提出一种高效的神经网络字符特征提取方法，从单字符的800个像素特征中仅提取211个特征向量。该方法识别率高（97．2％）、识别时间短（单字符0．02S和全车牌0．39s）、抗干扰和容错性强。在理论创新的基础上设计了LPR2011车牌识别系统，可应用于实际交通管制。

Abstr：License plate recognition is an important part of intelligent transportation systems with great significance for real time automatic road monitoring. The recognition accuracy rate is the core issue of the system. Existing methods are not effective due to noise and interference. This research describes license plate recognition method that combines a BP （back propagation） neural network and template matching. The method embeds template matching in the convergent neural network for further identification. The algorithm uses an efficient feature extraction method for the neural network which extracts only 211 features out of 800 pixels. The method achieves a high recognition rate of 97.2~ with rapid processing （0.02 s for single character and 0.39 s for an entire license plate）, resists interference and as tolerant to various faults. The system can be applied to actual traffic control.

文章关键词：

Keyword：:traffic control license plate recognition character recognition BP （back propagation） neural network template matching feature extraction

课题项目：国家自然科学基金资助项目（51008169）

作者信息：会员可见

  本文关键词：BP神经网络联合模板匹配的车牌识别系统，由笔耕文化传播整理发布。