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On purpose of identifying the license plate quickly and accurately from an well captured image, a license plate recognition scheme with the image super-resolution technology is proposed. License plate pictures have similar pattern features though coded with different numbers and characters. Therefore, license plate images are very suitable for super-resolution reconstruction. The system proposed in this paper is mainly composed of license plate detection and location, super-resolution reconstruction, character segmentation, character recognition and other modules. The three license plate detection strategies based on edge detection, color processing and maximum stability and extreme region algorithm are synthesized with parallel programming skills to get the candidate license plates. The support vector machine classifier is trained by using positive and negative samples of license plate images in advance. After the classifier is obtained, the real license plate is selected with the prediction model. The real license plate is then reconstructed with super-resolution technic. This stage is implemented mainly by the method based on the anchored neighborhood regression. This method combines the advantages of sparse dictionary learning and neighborhood embedding. Thus the accuracy and speed of calculation are both well taken into account. The OpenCV library is employed in the project to do character segmentation for the reconstructed image. An artificial neural network is then employed on the recognition stage. Before recognition, a certain number of positive and negative samples of character images are prepared to train the recognition models. In this paper, we use two single hidden layer neural networks and train with back propagation algorithm. After the network is fine tuned the features of the numbers and characters from test images are sent to the networks to complete the finial recognition. In order to test the performance of the system, one hundred pictures of the license plate collected from the actual scenes serve as the test set. Experiments show that the system has high recognition accuracy and fast processing speed.