Classification and comparison of license plates localization algorithms

TL;DR

This paper reviews and compares various algorithms for localizing license plates in images, highlighting their strengths, weaknesses, and recent improvements to enhance accuracy and robustness in diverse conditions.

Contribution

It provides a comprehensive classification and comparative analysis of license plate localization algorithms, identifying key advantages and areas for improvement.

Findings

Algorithms vary in accuracy and robustness under different conditions.

Certain methods outperform others in real-time processing scenarios.

Recent improvements have enhanced localization performance in challenging environments.

Abstract

The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the application of new information and communication technologies in the transport sector, to make the infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the key module of these systems, in which the License Plate Localization (LPL) is the most important stage, because it determines the speed and robustness of this module. Thus, during this step the algorithm must process the image and overcome several constraints as climatic and lighting conditions, sensors and angles variety, LPs no-standardization, and the real time processing. This paper presents a classification and comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages and improvements made by each of them