Automatic number plate recognition

Nayak, Veena

doi:10.30534/ijatcse/2020/195932020

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…A precise VNPT recognition method based on an OCR engine is suggested in [14]. The input license plate pic is converted to grayscale, subjected to global imagethresholding by utilizing Otsu technique, and had its noise removed.…”

Section: Literature Surveymentioning

confidence: 99%

Residential Vehicle Security System Using YOLO v5 Framework

Gaur,

Rakesh,

Sindhu

2023

Recent Trends in Data Science and Its Applications

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Security automation is essential today because it has become one of the main concerns for any organization. Many of the solutions that are currently available are still unreliable in real-world settings because they frequently depend on numerous constraints. In the project that follows, we will learn how to identify license plates using the Object localization and OCR. For this project, we'll use the YOLOv5 framework for license plate localization, then identify the license plate text using EasyOCR. This study will also show how a cutting-edge YOLO object detection-based ALPR system can be reliable and successful. The results show that a trained aneural network working with a high accuracy of about 90-95 percent can recognize license plates in low-resolution pictures. This project will present a robust and efficient ALPR system based on the state-of-the-art YOLO object detection. The results have shown that the trained neural network is able to perform with high accuracy of nearly 90-95 percent in recognizing licenseplates in low resolution images using this system.

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Section: Literature Surveymentioning

confidence: 99%

Residential Vehicle Security System Using YOLO v5 Framework

Gaur,

Rakesh,

Sindhu

2023

Recent Trends in Data Science and Its Applications

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“…In [14], a speedy and more accurate vehicle nameplate recognition system is proposed based on optical character recognition (OCR) engine "Tesseract". Raw images go undersampling and quantization.…”

Section: Review Of Previous Workmentioning

confidence: 99%

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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“…In the recognition phase, numbers and characters are automatically recognized from LP segments. To prevent plate numbers from being incorrectly recognized in the vehicle's image and vice versa, items in the plate are marked as letters or numbers depending on their position inside the plate [ 27 ]. The template matching system, for example, is used to identify different LPs in different countries.…”

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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Automatic number plate recognition

Cited by 12 publications

References 4 publications

Residential Vehicle Security System Using YOLO v5 Framework

Residential Vehicle Security System Using YOLO v5 Framework

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

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

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