A High-Performance Approach for Irregular License Plate Recognition in Unconstrained Scenarios

Abstract

This paper proposes a novel framework for locating and recognizing irregular license plates in real-world complex scene images. In the proposed framework, an efficient deep convolutional neural network (CNN) structure specially designed for keypoint estimation is first employed to predict the corner points of license plates. Then, based on the predicted corner points, perspective transformation is performed to align the detected license plates. Finally, a lightweight deep CNN structure based on the YOLO detector is designed to predict license plate characters. The character recognition network can predict license plate characters without depending on license plate layouts (i.e., license plates of single-line or double-line text). Experiment results on CCPD and AOLP datasets demonstrate that the proposed method obtains better recognition accuracy compared with previous methods. The proposed model also achieves impressive inference speed and can be deployed in real-time applications.