Electronic Number Plate Generation for Performance Evaluation

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICCST52959.2022.9896515The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology