图像超分辨率在模糊车牌识别系统中的应用

Application of Image Super-resolution in Fuzzy License Plate Recognition System

车牌识别技术种类繁多,理想情况下识别率已达到99%,而对于远距离模糊不清的抓拍图片,识别效果还不够,为此提出一种利用图像超分辨率重建技术提高模糊车牌识别率的方法。首先利用图像处理方法对图片进行分割;其次利用支持向量机(SVM)对分割得到的图块进行分类,筛选出车牌图块;再利用多帧低分辨率车牌图块进行最大后验估计(MAP)超分辨率重建,得到比较清晰、便于识别的车牌;最后利用人工神经网络(ANN)方法进行光学字符识别(OCR),最终得到识别结果。实验表明,与传统车牌识别技术相比,该方法具有更强的鲁棒性,且在模糊车牌识别中正确率明显提高。

Nowadays plate recognition technology is blooming everywhere.Some people even claim that their recognition rate has reached 99%.However,those are some of the ideal cases of recognition rate,for those long-distance blurred snapped pictures,their methods will seem powerless.In this paper,a method of using image super-resolution reconstruction technology to improve the recognition rate of fuzzy license plates is proposed.This method first uses image processing to segment the image,and then uses support vector machine(SVM)to classify and screen out the license plate tiles,and then uses the multi-frame low-resolution plate tiles to perform maximum a posteriori(MAP)estimation super-resolution reconstruction and obtain a relatively clear and easy recognition of the plate, and finally use the artificial neural network(ANN)method for optical character recognition(OCR)to get the plate recognition results. Experiments show that this method is more robust than the traditional license plate recognition technology,and the correct rate is significantly improved.