License Plate Recognition Techniques: Comparative Study

Abstract

Vehicle License Plate Recognition (LPR) has become a crucial system for various applications such as security monitoring, parking access, law enforcement and so on. LPR is employed for the identification of vehicles using their license plate. Recently, LPR technology has evolved progressively where vast improvement had been made towards the development of the image recognition’s quality and speed, as well as its state of the art methods. Although several research studies managed to resolve most of the issues that arise in LPR systems, more studies need to be conducted to improve the performance of LPR. This paper aims to provide a comprehensive analysis and comparison of different methods used in LPR. It summarizes each of the methods in terms of their accuracy, performance, strengths and weaknesses. Based on the recognition techniques used, LPR is then characterized into two categories, namely Traditional Computer Vision and Deep Learning Techniques.

Tesseract OpenCV Versus CNN: A Comparative Study on the Recognition of Unified Modern Iraqi License Plates

“…CNN's strength lies in playing a crucial role in improving the accuracy and efficiency of license plate recognition systems [16]. and from the deep learning techniques CNN has shown a promising result in achieving more efficient and optimal performance in LPR through her training with the training dataset used for the recognition of license plates in certain conditions, such as the non-standard design of the license plate, different environments, lighting, and weather conditions [17]. while the Weakness is Difficulty in recognizing multiple license plates within a single frame, especially when plates are in proximity or crowded areas.…”