[1]刘静.基于最小二乘支持向量机车牌字符特征识别[J].计算机技术与发展,2013,(05):195-198.
　LIU Jing.LSSVM-based License Plate Character Feature Recognition[J].,2013,(05):195-198.
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基于最小二乘支持向量机车牌字符特征识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
195-198
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
LSSVM-based License Plate Character Feature Recognition
文章编号:
1673-629X（2013）05-0195-04
作者:
刘静
渭南师范学院 统计科学与社会计算研究所
Author(s):
LIU Jing
关键词:
车牌识别; 最小二乘支持向量机; 奇异值分解
Keywords:
license plate recognition; least square support vector machine (LSSVM); singular value decomposition (SVD)
文献标志码:
A
摘要:
车牌识别通常按照车牌图像预处理、定位与字符分割、特征提取、特征分类的步骤展开分析与研究,特征提取与分类识别是提高车牌识别率和识别速度的关键环节.最小二乘支持向量机是一种新的有效的机器学习算法,文中提出利用最小二乘支持向量机识别车牌字符奇异值特征的方法.该方法是在车牌图像预处理的基础上,提取分割后车牌字符的奇异值特征,压缩保留主要特征,利用最小二乘支持向量机分类器分类识别.在自建车牌图像库上进行实验,结果证实,文中提出的方法是有效可行的
Abstract:
LP recognition is analyzed and researched in LP image preprocessing,location and character segmentation,feature extraction and feature classification. Among them,feature extraction and classification is the key link to reach more better recognition rate and speed. Least square support vector machine (LSSVM) is a kind of novel machine learning method. Propose a new method of license plate (LP) character recognition based on LSSVM and singular value decomposition (SVD). This method is based on preprocessing,extracts the singular value features of the LP character after segmentation,the main feature is compressed and contained,uses LSSVM to classify and identify. Experiment is based on LP image. The experimental results demonstrate the efficiency of the proposed approach

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更新日期/Last Update: 1900-01-01