一种基于GA和支持向量机的车牌字符识别方法

以高斯核为其核函数的支持向量机在实际应用中表现出优良的学习性能，被广泛应用于模式分类中。支持向量机的识别性能对参数的选取是敏感的，惩罚因子C和核函数参数σ对支持向量机性能会产生重要的影响。针对高斯核支持向量机在车牌字符识别问题中的应用，提出了一种基于遗传算法的参数选择方法。首先确定合适的遗传算法适应度函数，然后利用遗传算法对支持向量机的参数进行优化，最后在各个识别子网中分别采用参数优化后的支持向量机对车牌字符进行识别。实验结果表明，该方法取得了令人满意的识别率。

Vehicle license plate characters recognition based on genetic algorithms & support vector machines

The Support Vector Machines（SVM） with Gauss kernel function is widely used in pattern recognition because of its excellent properties.However，the performance of SVM with Gauss kernel is influenced greatly by the penalty parameter C and the scale parameter σ.A kind of method to select these parameters using genetic algorithms（GA） is proposed based on the study of vehicle license plate characters recognition.Firstly the appropriate fitness function for GA operation is determined，and then the parameters of SVM are selected by GA.Finally，the characters of the vehicle license plate are recognized by SVM with optimized parameters in various recognition sub-networks.The experimental results demonstrate the efficiency of the proposed approach.