Learning from Synthetic Data for Automatic License Plate Detection and Recognition

Automatic license plate detection and recognition (ALPDR) in natural scene is a useful but difficult task as the all-weather and variety of lighting conditions. Though deep learning based ALPDR methods can achieve much higher recognition rate, it needs a large number of human-labelled samples to train the deep neuron network. In this paper, we propose a method to generate synthetic data based CNN ALPDR to avoid manually labelling lots of data and stabilize training. First, our data engine generates 100K synthetic car license plates to simulate real scene and train networks. Then, we design a recognition network to predict all characters holistically, avoiding the character segmentation. Some real scene data sets are employed to validate the effectiveness of our presented method. The accuracy of our ALPDR system is 91.18% and 95% in toll station dataset and 94.2% in traffic surveillance dataset.