Recognizing License Plates in Real-Time

TL;DR

This paper presents a real-time license plate recognition system that combines contour detection, data augmentation, feature integration, transfer learning, and a two-phase verification process to achieve high accuracy and robustness under diverse conditions.

Contribution

The paper introduces a novel combination of techniques for real-time LPDR, including contour reconstruction, border removal, data augmentation, SIFT feature integration, transfer learning, and a two-phase verification, improving speed and accuracy.

Findings

System recognizes license plates accurately in real-time.

Robust under various lighting and noise conditions.

Outperforms peer schemes in speed and accuracy.

Abstract

License plate detection and recognition (LPDR) is of growing importance for enabling intelligent transportation and ensuring the security and safety of the cities. However, LPDR faces a big challenge in a practical environment. The license plates can have extremely diverse sizes, fonts and colors, and the plate images are usually of poor quality caused by skewed capturing angles, uneven lighting, occlusion, and blurring. In applications such as surveillance, it often requires fast processing. To enable real-time and accurate license plate recognition, in this work, we propose a set of techniques: 1) a contour reconstruction method along with edge-detection to quickly detect the candidate plates; 2) a simple zero-one-alternation scheme to effectively remove the fake top and bottom borders around plates to facilitate more accurate segmentation of characters on plates; 3) a set of techniques to augment the training data, incorporate SIFT features into the CNN network, and exploit transfer learning to obtain the initial parameters for more effective training; and 4) a two-phase verification procedure to determine the correct plate at low cost, a statistical filtering in the plate detection stage to quickly remove unwanted candidates, and the accurate CR results after the CR process to perform further plate verification without additional processing. We implement a complete LPDR system based on our algorithms. The experimental results demonstrate that our system can accurately recognize license plate in real-time. Additionally, it works robustly under various levels of illumination and noise, and in the presence of car movement. Compared to peer schemes, our system is not only among the most accurate ones but is also the fastest, and can be easily applied to other scenarios.