Recognizing License Plates in Real-Time
Xuewen Yang, Xin Wang

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…
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Taxonomy
TopicsVehicle License Plate Recognition · Handwritten Text Recognition Techniques · Advanced Neural Network Applications
