Automated License Plate Recognition: A Survey on Methods and Techniques

Shashirangana, Jithmi; Padmasiri, Heshan; Meedeniya, Dulani; Perera, Charith

doi:10.1109/access.2020.3047929

Cited by 97 publications

(58 citation statements)

References 94 publications

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“…Recently, deep learning architectures have also been used for the task of the LPD [23]. The CNN has been used in a cascaded form, where the first CNN classifier searches for any text on the image, and 2nd classifier is used to reject false positives, i.e., any other text from the text on LP [24].…”

Section: License Plate Detectionmentioning

confidence: 99%

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Performance enhancement method for multiple license plate recognition in challenging environments

Khan

Imran

Rehman

et al. 2021

J Image Video Proc.

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Multiple-license plate recognition is gaining popularity in the Intelligent Transport System (ITS) applications for security monitoring and surveillance. Advancements in acquisition devices have increased the availability of high definition (HD) images, which can capture images of multiple vehicles. Since license plate (LP) occupies a relatively small portion of an image, therefore, detection of LP in an image is considered a challenging task. Moreover, the overall performance deteriorates when the aforementioned factor combines with varying illumination conditions, such as night, dusk, and rainy. As it is difficult to locate a small object in an entire image, this paper proposes a two-step approach for plate localization in challenging conditions. In the first step, the Faster-Region-based Convolutional Neural Network algorithm (Faster R-CNN) is used to detect all the vehicles in an image, which results in scaled information to locate plates. In the second step, morphological operations are employed to reduce non-plate regions. Meanwhile, geometric properties are used to localize plates in the HSI color space. This approach increases accuracy and reduces processing time. For character recognition, the look-up table (LUT) classifier using adaptive boosting with modified census transform (MCT) as a feature extractor is used. Both proposed plate detection and character recognition methods have significantly outperformed conventional approaches in terms of precision and recall for multiple plate recognition.

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Section: License Plate Detectionmentioning

confidence: 99%

“…Recently, many methods and classification techniques have been proposed for recognition [23]. The template-matching method [4], calculates the correlation between character and templates and the maximum correlation valued template is considered as a character.…”

Section: Character Recognitionmentioning

confidence: 99%

Performance enhancement method for multiple license plate recognition in challenging environments

Khan

Imran

Rehman

et al. 2021

J Image Video Proc.

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show abstract

“…Give this class a discrete function in a coordinate system by f (x, y) like that [0, 0] that shows the top-left intersection of segmentation and bottom of the right corner represent of [1,1] width, height, w and h show segmentation dimensions. A 1 in f (x, y) represents black pixel and 0 represents white pixel space.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Predominantly, it is done manually by a human agent which is laborious, time-consuming, and erroneous. On the other hand, an automated system can identify vehicle license plates efficiently, in real-time [1]. It is very difficult to check all vehicles on the road through a manual approach.…”

Section: Introductionmentioning

confidence: 99%

License plate identification and recognition in a non-standard environment using neural pattern matching

Shafi

Hussain

Ahmad

et al. 2021

Complex Intell. Syst.

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Non-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

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“…e research on Automatic License Plate Recognition (ALPR) has been done for more than 20 years [1]. ALPR technology has also made considerable progress and has been widely used in different application fields and industries, such as automatic traffic violation detection [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

Tung

Wang

Peng

2021

Mathematical Problems in Engineering

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Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.

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Automated License Plate Recognition: A Survey on Methods and Techniques

Cited by 97 publications

References 94 publications

Performance enhancement method for multiple license plate recognition in challenging environments

Performance enhancement method for multiple license plate recognition in challenging environments

License plate identification and recognition in a non-standard environment using neural pattern matching

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

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