基于卷积神经网络的模糊车牌自动识别

目前,清晰的车牌识别算法已经成熟,但是对于人眼不能识别的模糊车牌,传统车牌识别算法的识别率较低或者根本无法识别。鉴于此,提出了一种基于卷积神经网络的车牌字符识别算法。制作了含9 720幅模糊字符样本集,用8 748幅样本对卷积神经网络进行训练,测试样本时,先对模糊车牌字符进行盲分割等预处理,再调用训练好的卷积神经网络对盲分割后的字符进行识别。实验结果表明:该算法对训练集的准确识别率约为99.17%,对测试集的准确识别率约为93.32%,这说明该算法对模糊车牌的识别具有鲁棒性,能应用于各种场景。

Automatic Fuzzy License Plate Recognition Based on CNN

The clear license plate recognition algorithm has already become a mature technology, but in terms of fuzzy license plate unable to be identified by human eyes, the recognition rate of traditional license plate recognition algorithm is still low or could not be identified at all. In view of this, a kind of license plate character recognition algorithm based on convolution neural network was proposed. 9 720 fuzzy character training samples were made, and 8 748 images were trained for convolutional neural network. The blind segmentation of fuzzy license plate characters was realized, and a trained convoluted neural network was used to recognize the characters after blind segmentation. The experimental results showed that the recognition rate of the algorithm reached about 99.17% for the training set, and the recognition rate of the test set to be about 93.32%, indicating that the algorithm was robust for fuzzy license plate recognition and could be applied to various scenes.