计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 586-590.DOI: 10.11772/j.issn.1001-9081.2016.02.0586

• 人工智能 • 上一篇    

基于梯度方向直方图与高斯金字塔的车牌模糊汉字识别方法

刘军, 白雪   

  1. 兰州理工大学, 机电工程学院, 兰州 730050
  • 收稿日期:2015-07-24 修回日期:2015-09-14 出版日期:2016-02-10 发布日期:2016-02-03
  • 通讯作者: 白雪(1990-),女,辽宁北镇人,硕士研究生,主要研究方向:车牌识别、移动目标的检测与跟踪。
  • 作者简介:刘军(1974-),男,甘肃陇南人,教授,博士,主要研究方向:复杂制造系统、生产调度与控制、精益生产。
  • 基金资助:
    国家自然科学基金资助项目(51265032)。

Fuzzy Chinese character recognition of license plate based on histogram of oriented gradients and Gaussian pyramid

LIU Jun, BAI Xue   

  1. College of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2015-07-24 Revised:2015-09-14 Online:2016-02-10 Published:2016-02-03

摘要: 针对现有车牌识别方法中对模糊车牌识别率不高的问题,提出一种结合高斯金字塔与梯度方向直方图(HOG)特征的车牌识别算法。利用金字塔模型多尺度表达的方法,首先对车牌模糊汉字图像建立两层高斯金字塔模型,第一层描述了模糊汉字的细节特征,通过对第一层作平滑处理并向下采样得到第二层,在描述模糊图像细节特征的基础上突出主体特征;然后对两层高斯金字塔模型提取HOG特征,拓展图像的特征维数,提高特征对模糊汉字的识别能力;最后采用BP神经网络分类器进行模糊且互为形近字间的汉字分类识别。仿真结果显示,在相同的样本空间下,与HOG特征法、K-L变换法相比,所提算法在识别率方面均有提高,能提高视频监控中对模糊汉字的有效识别率。

关键词: 车牌识别, 梯度方向直方图, 高斯金字塔, 形近字, BP神经网络

Abstract: Concerning the low recognition rate of fuzzy license plate in the existing license plate recognition method, a new license plate recognition algorithm combined with Gaussian pyramid and Histogram of Oriented Gradients (HOG) was proposed. Firstly, by utilizing the multi-scale expression of Gaussian pyramid, a two-layer Gaussian pyramid model was established for fuzzy Chinese character in license plate. Details about the fuzzy characters were described in the first layer. The second layer was obtained by taking smooth processing and down sampling on the first layer, and the main feature was highlighted by describing details of the fuzzy characters. By extracting HOG from two-layer Gaussian pyramid, the characteristic dimension of image was expanded and the ability of recognizing fuzzy Chinese characters was enhanced. Finally, fuzzy Chinese character in license plate was recognized by the Back Propagation (BP) neural network classifier. The simulation result shows that the recognition rate of the proposed method is higher than that of HOG feature method and K-L (Karhunen-Loeve) transform method in the same sample space, it means that the proposed method can improve the effective recognition rate of fuzzy Chinese characters in video surveillance.

Key words: license plate recognition, Histogram of Oriented Gradient(HOG), Gaussian pyramid, close character, Back Propagation(BP)neural network

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