A Robust and Efficient Approach to License Plate Detection

Abstract

This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization. Moreover, a cascaded license plate classifier based on linear SVMs using color saliency features is introduced to identify the true license plate from among the candidate regions.

Existing System

Neural Network was applied to extract color features from the hue, saturation and lightness channels separately. template-matching technique for localizing an Iranian license plate in an image through an analysis of target color pixels.

Proposed System

In this paper, a novel line density filter (LDF) is proposed to extract candidate license plate regions, thereby significantly reducing the area to be analyzed for license plate localization. An efficient license plate verification method is proposed to accurately detect the true license plate from among the candidate regions using a cascaded license plate classifier (CLPC), which is trained based on color saliency features. The proposed approach outperforms state-of-the-art methods by a large margin in terms of both detection accuracy and run-time efficiency.

Architecture

BLOCK DIAGRAM