License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study

TL;DR

This paper reviews various image processing and neural network techniques for license plate recognition, focusing on Malaysia, and demonstrates a MATLAB-based prototype for parking management systems.

Contribution

It provides a comprehensive review of LPR techniques and presents a MATLAB implementation tailored for Malaysia's license plates as a proof of concept.

Findings

Effective noise removal and image enhancement methods identified.

A MATLAB algorithm successfully recognized Malaysian license plates.

Prototype demonstrates feasibility for parking management applications.

Abstract

Most vehicle license plate recognition use neural network techniques to enhance its computing capability. The image of the vehicle license plate is captured and processed to produce a textual output for further processing. This paper reviews image processing and neural network techniques applied at different stages which are preprocessing, filtering, feature extraction, segmentation and recognition in such way to remove the noise of the image, to enhance the image quality and to expedite the computing process by converting the characters in the image into respective text. An exemplar experiment has been done in MATLAB to show the basic process of the image processing especially for license plate in Malaysia case study. An algorithm is adapted into the solution for parking management system. The solution then is implemented as proof of concept to the algorithm.