基于神经网络复杂背景下车牌识别系统的研究

车牌识别系统是以汽车车牌字符为目标对象的一个计算机视觉系统。由于获取的车辆图像受光照、噪声等外界因素的干扰，通常具有复杂性和不确定性，导致复杂场景下车牌字符识别更加困难。为提高识别率，首先利用BP网络对模糊处理后的定位车牌进行训练识别，利用MATLAB进行实验。再用卷积神经网络对车牌进行识别实验研究，与BP算法进行比较，对提出的算法进行仿真与实验，将两种方法进行对比发现看卷积神经网络算法对车牌字符识别率有较高的识别率，应用十分广泛。

License plate recognition system under complicated background based on neural network research

License plate recognition system based on car license plate character target of a computer vision system. Due to obtain the vehicle images affected by outside factors such as illumination and noise interference, usually with complexity and uncertainty, lead to complex scenarios license plate character recognition more difficult. In order to improve the recognition rate, this paper use BP network to the fuzzy processing is used to identify the training of the positioning plate, using Matlab to experiment. With convolution neural network for license plate recognition experiments, compared with BP algorithm, the simulation and experiment for the proposed algorithm, compare two methods found the convolutional neural network algorithm for license plate character recognition has high recognition rate, is widely used gradually.