License Plate Location and Recognition Based on Deep Learning

In some cases, the existing license plate recognition methods based on the character segmentation of license plates may fail in certain natural scenes such as dark illumination environments. In addition, the wrong character segmentation of the license plates directly affects license plate character recognition. In order to solve the above problems, a license plate location and recognition method based on deep learning is proposed in this paper. First, a license plate location method based on Faster R-CNN combining with the best license plate area selection using k-means++ is designed in this paper to solve the problem that the existing methods may fail in some natural scenes. Then, on the basis of the AlexNet network model, this paper reconstructs an enhanced convolution neural network model named AlexNet-L. AlexNet-L is an end-to-end network model for license plate character recognition, which can improve the accuracy of license plate recognition and avoid the problem that the wrong license plate character segmentation affects license plate recognition. The experimental results show that our proposed method can improve the accuracy and performance of license plate recognition.