Systematic review on vehicular licence plate recognition framework in intelligent transport systems

Arafat, Yeasir; Khairuddin, Anis Salwa Mohd; Khairuddin, Uswah; Raveendran, P.

doi:10.1049/iet-its.2018.5151

Cited by 24 publications

(7 citation statements)

References 82 publications

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“…This is an incredibly critical step in assessing the system's performance. The plates are detected throughout six important detection approaches [ 16 ], as detailed in Section 1 . (1) The first approach is the ability to distinguish the LP texture color transition, which is taken place between the background and letters.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Correct segmentation of license plate characters is important, as the majority of incorrect recognition is due to incorrect segmentation, as opposed to issues in the recognition process [29]. Several methods have been introduced for character segmentation.…”

Section: License Plate Character Segmentationmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

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Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

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“…With the rapid development of big data technology, artificial intelligence, machine learning, AI and other cutting-edge technologies, ITS is developing toward vehicle-road network collaboration, accurate real-time information acquisition, and artificial intelligence-aided decision-making. Data collection, communication and management are the core technical issues of current and future ITS development and construction [ 6 , 7 ]. While, the traffic chokepoints spread in the traffic network are the core data collection facilities in ITS.…”

Section: Introductionmentioning

confidence: 99%

A vehicle license plate data access model based on the jump hash consistency algorithm

Wang,

Cheng,

Chen

et al. 2023

PLoS ONE

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The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key–value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

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Systematic review on vehicular licence plate recognition framework in intelligent transport systems

Cited by 24 publications

References 82 publications

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

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

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

A vehicle license plate data access model based on the jump hash consistency algorithm

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