A Novel Method for Indian Vehicle Registration Number Plate Detection and Recognition using Image Processing Techniques

Varma, P. Ravi Kiran; Srikanth, Ganta; B, Hari Krishna; Svsrk, Praveen

doi:10.1016/j.procs.2020.03.324

Cited by 48 publications

(14 citation statements)

References 7 publications

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“…A state of art image processing technique for dealing with noisy and substandard Indian number plates, in [16], as previously presented models, performs well under controlled conditions such as clear text, fine illustration, and perfect angles. e shoddy images undergo morphological operations and Gaussian rectification as preprocessing.…”

Section: Review Of Previous Workmentioning

confidence: 83%

Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

et al. 2021

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The metropolis of the future demands an efficient Automatic Number Plate Recognition (ANPR) system. Since every region has a distinct number plate format and style, an unconstrained ANPR system is still not available. There is not much work done on Pakistani number plates because of the unavailability of the data and heterogeneous plate formations. Addressing this issue, we have collected a Pakistani vehicle dataset having various plate configurations and developed a novel ANPR framework using the dataset. The proposed framework localizes the number plate region using the YOLO (You Only Look Once) object detection model, applies robust preprocessing techniques on the extracted plate region, and finally recognizes the plate label using OCR (optical character recognition) Tesseract. The obtained mAP score of the YOLOv3 is 94.3% and the YOLOv4 model is 99.5% on the 0.50 threshold, whereas the average accuracy score of our framework is found to be 73%. For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. The comparative analysis shows that the proposed ANPR framework comprising the YOLOv4 and OCR Tesseract has good accuracy and inference time for a wide variation of illumination and style of Pakistani number plates and can be used to develop a real-time system. The proposed ANPR framework will be helpful for researchers developing ANPR for countries having similar challenging vehicle number plate formats and styles.

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Section: Review Of Previous Workmentioning

confidence: 83%

Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

et al. 2021

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“…In [61], a new system for detecting and recognizing the Indian vehicle number plate has been suggested by Varma that can compete with noisy, low-light, cross-angled, non-standard font number plates. This work uses many image processing techniques in the pre-processing stage, such as morphological transformation, Gaussian smoothing, and Gaussian thresholding.…”

Section: Related Work On Vehicle Number Plate Detection and Recognition Techniquesmentioning

confidence: 99%

Vehicle Number Plate Detection and Recognition Techniques: A Review

Parvin¹,

Rozario²,

Islam³

2021

Adv. sci. technol. eng. syst. j.

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Vehicle number plate detection and recognition is an integral part of the IntelligentTransport System (ITS) as every vehicle has a number plate as part of its identity. The quantity of vehicles on road is growing in the modern age, so numerous crimes are also increasing day by day. Almost every day the news of missing vehicles and accidents are perceived. Vehicles tracking is often required to investigate all these illegal activities. So, vehicle number plate identification, as well as recognition, is an active field of study. However, vehicle number plate identification has always been a challenging task for some reasons, for example, brightness changes, vehicle shadows, and non-uniform license plate character type, various styles, and environment color effects. In this review work, various state-of-the-art vehicle number plate detection, as well as recognition strategies, have been outlined on how researchers have experimented with these techniques, which methods have been developed or used, what datasets have been focused on, what kinds of characters have been recognized and how much progress have been achieved. Hopefully, for future research, this review would be very useful.

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“…The National Highways Authority of India is in charge of automobile registration is mandatory. As per their financial report, 3 the number of automobiles in India is steadily growing each year as depicted in Figure 1. This might explain the rising rate of road fatal crashes.…”

Section: Introductionmentioning

confidence: 98%

A hybrid location‐dependent ultra convolutional neural network‐based vehicle number plate recognition approach for intelligent transportation systems

Ramasamy¹,

Selvarajan²,

Kaliyaperumal

et al. 2023

Concurrency and Computation

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Summary In today's world, identifying the owner and proprietor of a vehicle that violates driving rules or does any unintentional work on the street is a challenging task. Inspection of each driver's license number takes a long time for a highway police officer. To overcome this, many researchers have introduced an automated number plate recognition approach which is usually a computer vision‐based technique to identify the vehicle's registration plate. However, the existing recognition approaches are lagged to extract the influential features which degrade the detection accuracy and increase the misclassification errors. In this article, a novel automated number plate recognition methodology has been proposed to identify the number plates accurately with minimal error rates. Primary, a new pretrained location‐dependent ultra convolutional neural network (LUCNN) is employed to learn the influential features from the input images. These obtained features are then fed into hybrid single‐shot fully convolutional detectors with a support vector machine (SSVM) classifier to separate the vehicle's city, model, and number from the registration location. At varied automobile distances, the proposed LUCNN + SSVM model is able to retrieve the number plate regions in the picture acquired from its back end. The performance results manifest that the proposed LUCNN + SSVM model attains a better accuracy of 98.75% and a lesser error range of 1.25% than the existing recognition models.

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A Novel Method for Indian Vehicle Registration Number Plate Detection and Recognition using Image Processing Techniques

Cited by 48 publications

References 7 publications

Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

Vehicle Number Plate Detection and Recognition Techniques: A Review

A hybrid location‐dependent ultra convolutional neural network‐based vehicle number plate recognition approach for intelligent transportation systems

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