  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.
