Multi-task Learning for Low-Resolution License Plate Recognition

Abstract

License plate recognition is an important task applied to a myriad of important scenarios. Even though there are several methods for performing license plate recognition, our approach is designed to work not only on high resolution license plates but also when the license plate characters are not recognizable by humans. Early approaches divided the task into several subtasks that are executed in sequence. However, since each task has its own accuracy, the errors of each are propagated to the next step. This is critical in the last two steps of the pipeline known as segmentation and recognition of the characters. Thus, we employ a technique to perform these two steps at once. The approach is based on a multi-task network where each task represents the recognition of an entire license plate character. We do not address the license plate detection problem in this paper. We also propose the use of a so called generative model for data augmentation of low-resolution images simulating images as if they were acquired farther away from where they actually are. We are able to achieve very promising results with improvements of more than 30% points of accuracy on images with multiple resolutions and a character recognition accuracy on low-resolution images higher than 87%.