Robust Automatic Recognition of Chinese License Plates in Natural Scenes

• Published in: IEEE access 2020, Vol.8, p.173804-173814
• Main Authors: He, Ming-Xiang, Hao, Peng
• Format: Article
• Language: English
• Subjects: Affine transformations; Automatic vehicle identification systems; Character recognition; Convolutional neural networks; Deep learning; Distortion; Feature extraction; Image edge detection; Image recognition; Intelligent transportation systems; license plate detection; License plate recognition; License plates; Object recognition; Robustness; Weather
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3026181

Automatic license plate recognition has a wide range of applications in intelligent transportation systems and is of great significance. However, most of the current work on license plate recognition focuses on the images on the front of license plates. license plate recognition in natural scenes and arbitrary perspective is still a huge challenge. To solve this problem, this work mainly studies the detection and recognition of inclined Chinese license plates in natural scenes. We propose a robust method that can detect and correct multiple license plates with severe distortion or skewing in one image and input them into the license plate recognition module to obtain the final result. Different from the existing methods of license plate detection and recognition, our method performs affine transformation during license plate detection to rectify the distorted license plate image. It can not only avoid the accumulation of intermediate errors but also improve the accuracy of recognition. As an additional contribution, we put forward a challenging Chinese license plate recognition data set, including images obtained from different scenes under a variety of weather conditions. Through a large number of comparative experiments, we have proved the effectiveness of our proposed method.