Novel Algorithm for Iraqi Car License Plate Detection and Recognition

Aldabbagh, Ali H. A.; Al-Shimaysawee, Laith A. H.; Al-Rikabi, Hussein M. H.

doi:10.36478/jeasci.2019.205.210

Cited by 1 publication

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“…As a result, Iraqi and Malaysian LPs are taken into account in this paper. While numerous studies have been carried out on Malaysian LPs [ 7 , 10 , 11 ], there have been very few studies on Iraqi LPs using conventional machine learning methods [ 12 – 14 ]. There has been no research into using deep learning for all Iraqi regions.…”

Section: Introductionmentioning

confidence: 99%

Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb

Noman

Alkahtani

et al. 2021

Computational Intelligence and Neuroscience

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Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

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