Vehicle License Plate Recognition System Research Articles

Vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali

Vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali

Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems.

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference.

Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates' Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models' accuracy by taking advantage of the temporally redundant information of the video stream's frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.

An efficient automated vehicle license plate recognition system under image processing

<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.</span>

Rancang Bangun Sistem Pengenalan Plat Nomor Kendaraan Menggunakan Jaringan Saraf Tiruan Backpropagation

The manual parking system allows for errors in recording, the service takes a long time, and there is no history of vehicle users. The vehicle license plate recognition system is designed as an alternative parking system that is more accurate in recording, fast service, and the presence of vehicle user data. Vehicle license plate recognition system has been designed equipped with Backpropagation Artificial Neural Network (ANN). The recognition system that has been designed in advance goes through an image processing process with grayscale, black and white, segmentation, and principal component analysis (PCA) stages. The system is integrated with backpropagation ANN with multi layer network with the best results at layer1 550 and layer2 500. The number plate recognition system is equipped with a Graphycal User Interface (GUI) to display the identification results of vehicle number plate identification. The results of identification number plate recognition resulted in 100% training accuracy and 97.95918367% testing accuracy.

An All-in-One Vehicle Type and License Plate Recognition System Using YOLOv4.

In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things.

Vehicle License Plate Recognition

One of the most significant parts of integrating computer technologies into intelligent transportation systems (ITS) is vehicle license plate recognition (VLPR). In most cases, however, to recognize a license plate successfully, the location of the license plate is to be determined first. Vehicle License Plate Recognition systems are used by law enforcement agencies, traffic management agencies, control agencies, and various government and non-government agencies. VLPR is used in various commercial applications, including electronic toll collecting, personal security, visitor management systems, parking management, and other corporate applications. As a result, calculating the correct positioning of a license plate from a vehicle image is an essential stage of a VLPR system, which substantially impacts the recognition rate and speed of the entire system. In the fields of intelligent transportation systems and image recognition, VLPR is a popular topic. In this research paper, we address the problem of license plate detection using a You Only Look Once (YOLO)-PyTorch deep learning architecture. In this research, we use YOLO version 5 to recognize a single class in an image dataset.

Super-resolution Restoration of Single Vehicle Image Based on ESPCN-VISR Model

In the process of image acquisition monitoring in vehicle license plate recognition system, the accuracy of license plate recognition will decrease which is caused by the complicated application scenarios, poor performance of the equipment and system, and the degeneration of surveillance images. In order to overcome the shortcoming, a super-resolution restoration method for single vehicle image based on efficient sub-pixel convolutional neural network (ESPCN) model is proposed (hereinafter referred to as ESPCN-VISR method). In the experiments, ESPCN-VISR method was proved to be superior by four indexes of quantitative assessment which are peak signal to noise ratio (PSNR), structural similarity (SSIM), vehicle license plate recognition accuracy (VLPRA), and calculation time of reconstruction (CTR). Compared with the methods based on sparse dictionary learning and deep convolutional neural network SRCNN, the ESPCN-VISR method can improve the performance of vehicle license plate recognition system with vehicle license plate sample image dataset LPI-1000.

Vehicle License Plate Recognition System Based on Deep Learning in Natural Scene

With the popularity of intelligent transportation system, license plate recognition system has been widely used in the management of vehicles in and out of closed communities. But in the natural environment such as video monitoring, the performance and accuracy of recognition are not ideal. In this paper, the improved Alex net convolution neural network is used to remove the false license plate in a large range of suspected license plate areas, and then the projection transformation and Hough transformation are used to correct the inclined license plate, so as to build an efficient license plate recognition system in natural environment. The proposed system has the advantages of removing interference objects in a large area and accurately locating the license plate. The experimental results show that the localization success rate is 98%, and our system is feasible and efficient.

Perspective Vehicle License Plate Transformation using Deep Neural Network on Genesis of CPNet

Abstract Recent development in vehicular industries and increased number of cars in modern society leads the people to pay more attention on Vehicle License Plate Recognition (V-LPR). V-LPR plays a major role in traffic related application such as road traffic monitoring, vehicle parking lots access control etc. Existing state of the art V-LPR systems in real world deployment works under restricted conditions, such as static illumination, fixed background etc. Most of them fails to work when any of the above given conditions are violated. Hence to address this issue, a novel V-LPR system is designed using modern deep learning framework called Capsule Network. The proposed system is robust and works fine in any condition. Further, the proposed method aims to improve the processing time by integrating the segmentation process within the CN framework which involves the training and recognizing of entire license plate cropped region. Moreover, the feature extraction is performed by CN framework over a segmented alphanumeric character. Finally, Data augmentation technique is also used as a supplement to the CN framework to strengthen the process of training with various orientations like rotation, shift and flip for improving the recognition task.

Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching

With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system.

A Character Segmentation Algorithm for Vehicle License Plates

An efficient computational method of segmentation of license plate characters is considered. Division into blocks is carried out on the assumption that the characters are localized in certain specific areas. The license plate image is divided into n parts, where n is the number of characters. The paper also describes a segmentation algorithm that underlies a modified version of this algorithm. Contour analysis was used as a mathematical apparatus for the solution of this problem. The proposed character segmentation algorithm allows its use in vehicle license plate recognition systems.

Research on Vehicle Track Tracking and Vehicle Reverse Lookup Algorithm Based on Ultrasonic Waveform Recording

In order to solve the problems of garage expand, video based searching costs high, ultrasonic detector based searching cannot achieve. This paper presents a vehicle trajectory tracking algorithm based on ultrasonic waveform recording. Parking management system can determine the parking position through the ultrasonic detector, so as to achieve vehicle reverse lookup. The system first uses the vehicle license plate recognition system to obtain the vehicle information, and sets the ultrasonic detector at the intersection to perceive the detection time and waveform recording signal. Firstly according to the time series determine the direction of the vehicle at the intersection. Then via the ultrasonic wave curve of two directions at the intersection to Identify the vehicle profile and determine the turning direction. Finally, the car's parking position is tracked through the parking space detector. This paper developed the WeChat public number for vehicle reverse lookup and payment. The user concerned to find car only need to enter the license plate number and can pay online fare. The system has been tested to be stable and reliable, which is a feasible solution to realize vehicle reverse searching at low cost.

Adaptive enhancement and binarization techniques for degraded plate images

Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4 k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.

Iraqi Kurdistan Vehicle License Plate Recognition System based on Client-server Network

Vehicle license plate recognition (VLPR) system has a wide range of applications in real life, such as parking lots, private and public entrances, theft and border control. This paper presents a system overview of client-server license plate recognition system for vehicles in Kurdistan Region of Iraq besides, the vehicle type and city in each plate is identified. The client/server model consist of two components called client-side and server-side. The images are provided by the client side and will be sent over the server to be processed. The process of license plate recognition is done in the server side with the integration with the Matlab software. The proposed recognition system is developed based on digital images and the main component of the system consist of: preprocessing, license plate detection, segmentation and recognition process. A preprocessing step using image enhancement, filtering, and adaptive thresholding is employed. For localizing and extracting the license plate region, Haar wavelet transformation and edge detection algorithm are used. license plate number segmentation is carried out using bounding box based morphological operations and finally color histogram technique and correlation coefficient based similarity measurement are used for recognition task. The experimental results show that our proposed system for the license plate detection and recognition process have obviously higher recognition accuracy rate.

GUI기반 산업용 디지털 기기의 측정값 인식 시스템

본 논문에서는 영상처리를 통해 GUI를 기반으로 산업용 디지털 기기의 측정값을 인식하고 기록하는 시스템을 제안하고 구현하였다. 제안한 시스템은 기존의 차량번호판 인식과 달리 산업용 측정기의 LCD화면에 표시되는 값은 디지털 숫자로 표시하고 있어 소수점과 마이너스 표시, LCD보호유리의 반사광등의 여러 가지 장애요인을 고려하였다. LCD화면에 표시된 숫자를 인식하기 위해 블롭 레이블링 (blob-labeling)기법을 사용하였으며, 인식한 숫자 이미지는 템플릿 매칭(template matching)을 통해 숫자가 무엇인지 판별하여, 인식한 측정값을 측정시간과 함께 저장장치에 기록하였다. 본 논문에서 제안한 시스템은 산업현장에서 제품의 내외경이나 높이를 측정하고 기록할 때 수기로 작성하는 번거로움을 줄이고, 수기로 작성 시 잘못 기입하는 경우를 방지함으로써 생산 공정 과정에서 오류가 없는 효율적인 공정관리가 가능하게 하였다. In this paper, we proposed and implemented a GUI-based system to recognize and record measured values of digital instruments in the industrial site through image processing. Unlike the existing vehicle license plate recognition system, the measured values of the measuring instrument are displayed on the LCD screen as digital numbers. So, the proposed system considers the decimal point, a negative sign, light reflected by LCD protective glass, and various disturbance factors. We used blob-labeling technique to recognize the numbers displayed on the LCD screen, the recognized number images were determined as certain numbers through the template matching, and recognized values were recorded in the storage device with measurement time. Therefore, the proposed system in this paper would reduce the burden of writing when recording the measured values of the inside/outside diameter or height of the product in the industrial site, so effective and errorless process management in production process is possible by preventing errors in recording measurements when written by hand.

A Robust Method for Vehicle License Plate Recognition based on Harries Corner Algorithm and Artificial Neural Network

Automatic vehicle license plate recognition system plays an important role in Intelligent Transportation System. Even though many license plate recognition methods have been proposed in the past, further scope for improvement still exists. This paper has proposed a new approach to license plate recognition system based Harris corner detection algorithm. In this paper connected component analysis is used for character segmentation and artificial neural networks for character recognition. It is observed from the experiments that the developed system successfully identifies and recognizes the vehicle number plate under different illumination conditions and independent of orientation and scale of the

The research and design of vehicle license plate recognition system in traffic management system

The research and design of vehicle license plate recognition system in traffic management system

Vehicle License Plate Detection and Recognition System

This is a survey paper on the Vehicle License Plate Detection and Recognition System. This system works in five phases. The first phase is the input given to the system which is an image of the vehicle with clearly visible license plate. The second phase is the localization of the license plate from the image. The third phase is the segmentation of the characters in the localized license plate. In the fourth phase the segmented characters are recognized using Artificial Neural Networks. In the fifth and final phase these recognized characters are stored in a database for further reference. General Terms Automatic License Plate Recognition (ALPR), MATLAB, Neural Networks

Nigerian Vehicle License Plate Recognition System using Artificial Neural Network

In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) system using artificial neural network is done. Vehicle License Plate Recognition (VLPR) is a special form of optical character recognition (OCR) which enables computer systems to read automatically the registration number of vehicles from digital pictures for the purpose of traffic control, security, access control to restricted areas, tracking of cars, tracing of stolen cars, identification of dangerous and reckless drivers on the road. This system is divided into three major parts: vehicle license plate detection, vehicle license plate character segmentation and License Plate character recognition. In vehicle license plate detection there are many challenges such as, complex plate background, illumination in consistencies, vehicle motion, distance changes for which edge detection analysis was explored. In this work, 200 vehicle license plates were captured, some with clear characters, others with blur and dirty stains . The character feature extraction and plate recognition accuracies were determined. Results showed that plates without blur and stain were most accurately extracted and recognized while satisfactory results were also obtained for the others.