集装箱箱号字符识别关键技术的研究
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摘要
文中主要以集装箱箱号识别为研究对象,在对现有的集装箱照片进行预处理以及定位的基础上,实现箱号的准确识别。主要论述影响字符识别各种关键因素,并且在传统模板匹配的基础上,提出全新的加权模板匹配法和字符结构相结合的识别方法。该方法并不是将36个字符用统一的模板进行匹配识别,而是根据字符结构进行分类,每类都会予以不同的权值分配模板。字符结构是一种二级识别,两种方法相结合很好地实现了对断裂、粘连以及倾斜字符的识别。最后通过理论分析和实验证明了该方法的有效性,并给出了需要进一步研究的问题。
Taking the recognition of container numbers as the research object,the accurate recognition of container numbers is realized on the basis of preprocessing and positioning of the existing container pictures. Various key factors affecting character recognition are mainly discussed. A new recognition method by combining the weighted template matching method with character structure is proposed on the basis of traditional template matching. In the method,it is not to use a unified template to conduct matching recognition of 36 characters. Instead,the characters are classified according to character structures,and each class is assigned a different weight assignment template. Character structure recognition is a kind of secondary recognition. The combination of the two methods can realize the recognition of broken,glued,and slanted characters. The effectiveness of the method is proved by means of theoretical analysis and experiment. The problems that need further study are given.
Taking the recognition of container numbers as the research object,the accurate recognition of container numbers is realized on the basis of preprocessing and positioning of the existing container pictures. Various key factors affecting character recognition are mainly discussed. A new recognition method by combining the weighted template matching method with character structure is proposed on the basis of traditional template matching. In the method,it is not to use a unified template to conduct matching recognition of 36 characters. Instead,the characters are classified according to character structures,and each class is assigned a different weight assignment template. Character structure recognition is a kind of secondary recognition. The combination of the two methods can realize the recognition of broken,glued,and slanted characters. The effectiveness of the method is proved by means of theoretical analysis and experiment. The problems that need further study are given.
引文
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[5]邹明明,卢迪.基于改进模板匹配的车牌字符识别算法实现[J].国外电子测量技术,2010,29(1):59-61.ZOU Mingming,LU Di. Recognition algorithm of car license plate characters based on modified template match[J]. Foreign electronic measurement technology,2010,29(1):59-61.
[6]龚廷恺,袁晓辉,王细洋.最小熵反褶积的数学形态法在滚动轴承故障特征提取中的应用[J].中国机械工程,2016,27(18):2467-2471.GONG Tingkai,YUAN Xiaohui,WANG Xiyang. Applications of mathematical morphology method to fault feature extraction of rolling bearings based on minimum entropy deconvolution[J]. China mechanical engineering,2016,27(18):2467-2471.
[7]吕秀丽,谷立冬,祝立群,等.基于数学形态学和垂直投影的车牌定位[J].科学技术与工程,2012,12(10):2465-2467.LüXiuli,GU Lidong,ZHU Liqun,et al. License plate localization based on mathematical morphology and vertical projection[J]. Science technology and engineering,2012,12(10):2465-2467.
[8]郑宗良.基于模糊判别准则和特征加权模板匹配的字符识别[J].科学技术与工程,2012,12(23):5794-5796.ZHENG Zongliang. Character recognition based on fuzzy identification criterion feature weight template matching[J]. Science technology and engineering,2012,12(23):5794-5796.
[9]朱俊梅,陈少平.基于改进的投影方法的车牌图像字符分割[J].中南民族大学学报(自然科学版),2007,26(4):58-61.ZHU Junmei,CHEN Shaoping. Segmenting characters of license plate based on improved projection[J]. Journal of SouthCentral University for Nationalities(Natural science edition),2007,26(4):58-61.
[10]王永茂,刘贺平.强噪声车牌图像中的字符识别[J].计算机仿真,2006,23(12):244-246.WANG Yongmao,LIU Heping. Character recognition of plate license with strong noise[J]. Computer simulation,2006,23(12):244-246.
[2]沈清波,常龙昆.基于字符特征叠加提取与BP神经网络的字符识别[J].辽宁石油化工大学学报,2014,34(6):74-78.SHEN Qingbo,CHANG Longkun. Research on character recognition based on repeated character feature extraction and BP network[J]. Journal of Liaoning University of Petroleum&Chemical Technology,2014,34(6):74-78.
[3]罗辉武,唐远炎,王翊,等.基于结构特征和灰度特征的车牌字符识别方法[J].计算机科学,2011,38(11):267-270.LUO Huiwu,TANG Yuanyan,WANG Yi,et al. License plate character recognition based on structural features and grayscale pixel features algorithm[J]. Computer science,2011,38(11):267-270.
[4]吴晓军,邹广华.基于边缘几何特征的高性能模板匹配算法[J].仪器仪表学报,2013,34(7):1462-1469.WU Xiaojun, ZOU Guanghua. High-performance template matching algorithm based on edge geometric features[J]. Chinese journal of scientific instrument,2013,34(7):1462-1469.
[5]邹明明,卢迪.基于改进模板匹配的车牌字符识别算法实现[J].国外电子测量技术,2010,29(1):59-61.ZOU Mingming,LU Di. Recognition algorithm of car license plate characters based on modified template match[J]. Foreign electronic measurement technology,2010,29(1):59-61.
[6]龚廷恺,袁晓辉,王细洋.最小熵反褶积的数学形态法在滚动轴承故障特征提取中的应用[J].中国机械工程,2016,27(18):2467-2471.GONG Tingkai,YUAN Xiaohui,WANG Xiyang. Applications of mathematical morphology method to fault feature extraction of rolling bearings based on minimum entropy deconvolution[J]. China mechanical engineering,2016,27(18):2467-2471.
[7]吕秀丽,谷立冬,祝立群,等.基于数学形态学和垂直投影的车牌定位[J].科学技术与工程,2012,12(10):2465-2467.LüXiuli,GU Lidong,ZHU Liqun,et al. License plate localization based on mathematical morphology and vertical projection[J]. Science technology and engineering,2012,12(10):2465-2467.
[8]郑宗良.基于模糊判别准则和特征加权模板匹配的字符识别[J].科学技术与工程,2012,12(23):5794-5796.ZHENG Zongliang. Character recognition based on fuzzy identification criterion feature weight template matching[J]. Science technology and engineering,2012,12(23):5794-5796.
[9]朱俊梅,陈少平.基于改进的投影方法的车牌图像字符分割[J].中南民族大学学报(自然科学版),2007,26(4):58-61.ZHU Junmei,CHEN Shaoping. Segmenting characters of license plate based on improved projection[J]. Journal of SouthCentral University for Nationalities(Natural science edition),2007,26(4):58-61.
[10]王永茂,刘贺平.强噪声车牌图像中的字符识别[J].计算机仿真,2006,23(12):244-246.WANG Yongmao,LIU Heping. Character recognition of plate license with strong noise[J]. Computer simulation,2006,23(12):244-246.