See-Lpr: A Semantic Segmentation Based End-To-End System For Unconstrained License Plate Detection And Recognition

Most previous works regard License Plate detection and Recognition (LPR) as two or more separate tasks, which often leads to error accumulation and low efficiency. Recently, several new studies use end-to-end training to overcome these problems and achieve better results. However, challenges like misalignment and variable-length or multi-language LPs still exist. In this paper, we propose a novel Semantic segmentation based End-to-End multilingual LPR system SEE-LPR to solve these challenges. Our system has four components which are convolution backbone, LP capture, LP alignment, and LP recognition. Specifically, LP alignment is used to connect LP capture and LP recognition, allowing the gradient back-propagate through the whole network and can handle oblique LPs. Connectionist Temporal Classification (CTC) module used in LP recognition makes our system able to handle LPs with variable-length or multi-language. Comparative studies on several challenging benchmark datasets show that the proposed SEE-LPR system significantly outperforms the state-of-the-art systems in both accuracy and efficiency.