Number Plate Recognition System Research Articles

A study of the automated vehicle number plate recognition system

Automatic Number Plate Recognition (ANPR) uses number plates to identify vehicles. The goal of an automated vehicle identification system is to identify the vehicle based on the number plate. The system enforces the regulations, parking, etc. It can also be used at the entrance to protect a large area, such as a military zone or the region around important government buildings like the military base, Parliament, Supreme Court, etc. The smart technology recognizes and captures the image of the vehicle. The number plate area of the vehicle is extracted using image segmentation on the image. Optical character recognition is used for character recognition. The performance data may also be compared to database records to determine the car owner, enrollment location, residence, etc. The testing showed that the improved algorithm easily recognized the number plate of a vehicle on genuine photographs.

The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks

The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.

Automatic Number Plate Recognition Using Image Processing

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.

Traffic Violation Detection System Using Image Processing

Over the last three decades, the global population of human beings has increased at an exponential rate, resulting in an equal rise in the number of vehicles owned and used globally. Vehicle traffic is a major economic component in both urban and rural areas, and it requires proper management and monitoring to ensure that this mass of vehicles coexists as smoothly as possible. The amount of vehicular traffic on roads around the world, with Nigeria as a case study, results in varying degrees of traffic rule violations, especially red light jumping. To arrest offenders and resolve the weaknesses and failures of human traffic operators who cannot be everywhere at once, efficient traffic violation and number plate recognition systems are needed. There are several methods for reading characters, which can be alphabets, numbers, or alphanumeric. To minimize processing time and computational load on the machine, this research proposed k-Nearest Neighbour for plate number character recognition. The system was developed and evaluated. From the result, the localization of license plate regions within an image was 92 percent accurate, and character recognition was 73 percent accurate.

Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

License Plate Detection and Recognition using Convolutional Neural Network

Vehicle Number Plate Detection and Recognition system are very helpful and necessary for efficient traffic monitoring and management. From various angles, the interested vehicle's images can be captured by using some of the strong image processing technique devices for the growth of the smart transport system. Identifying the number plates with the side view images, images with moving distance, numbering, and the number plate type by identifying the background can be improved by using a recognition technique that may contain the combination of the image processing technology combined with neural networks. The tilted images and side view images of moving vehicles can be identified by using a neural network. This can be further useful for the identification of vehicle owners, vehicle model identification, controlling the traffic, controlling the vehicle speed, and tracking of vehicle location. Some algorithms like super-resolution of images can be used for the improvement of low-resolution images. In the future, to gain a better accuracy rate, high-resolution cameras can be used which contain high frames.

Performance Analysis on Automatic Number Plate Recognition System and Methods

Now a days, vehiclesareentering the educational institutes or other organizations without taking a proper record. It is necessary to implement a system which is used to detect the vehicle number plate and recognize it, is this vehicle registered in this institute or not. Automatic Number Plate Recognition (ANPR) System used to resolve this issue. This system is useful to Parking fee collection, Barrier crossing, Finding the stolen cars and so on. Some condition may be affectthis due to low light or weather condition, size of thenumber plate also matter. This paper showsthe multiple techniques to implement this system and also show the results of Automatic Number Plate Recognition system

Number plate recognition on vehicle using YOLO - Darknet

Character recognition is one of the steps in the number plate recognition system. Character recognition is done to get text character data. The method used is YOLOv3 (You Only Look Once), and Darknet-53 is used as a feature extractor. In this study, the data used were number plate images derived from the extraction and cropping of motorized vehicle videos that had been taken using cellphones and cameras. Testing is done with two different models, namely the model obtained with additional preprocessing data and the model obtained without any preprocessing data. Data preprocessing is done to improve the quality of the number plate image. Testing is done on an uninterrupted number plate image dataset and a number plate image dataset with interference and a reduction of color intensity (brightness) in the image. For the model obtained from the data without preprocessing, the highest number plate recognition accuracy obtained is 80%, and the character recognition accuracy is 97.1%. Meanwhile, for the model obtained from preprocessing data, the highest number plate recognition accuracy obtained was 88%, and the character recognition accuracy was 98.2%.

AUTOMATIC NUMBER PLATE RECOGNITION FOR DIFFERENT FONTS AND NON-ROMAN SCRIPT

According to survey taken the total number of vehicles in [1] India were 260 million. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) systems [1] in India because of the large number of vehicles travelling on the roads. [1] It would also help in proper tracking of the vehicles, traffic examining, finding stolen vehicles, supervising parking toll and imposing strict actions against red light breaching. Automatic number plate recognition is image processing technique for finding number plate from image and extracting characters from detected number plate. ANPR in India has always been challenging due to different lighting conditions, changes in fonts, shapes, angles, letters size, number of lines and padding between lines, different languages used. In our project we proposed a model that can detects number plate with considering all irregularities. this system uses Computer vision and machine learning technology in order to detect number plate from image. In our proposed system number plate can be of different fonts and non-roman script. For identification of characters from number plate we use OCR (Optical character recognition) technique. OCR involves two parts: Character segmentation and Character Recognition. This OCR system can be used to extract characters of different fonts and non-roman script. The Quality of OCR depends on the quality of image, image contrast, text font style and size. To improve quality of OCR we can use image processing technique to enhance quality of image.

Automatic Vehicle Number Plate Recognition System Using Machine Learning

The number plate recognition (NPR) system is one of the categories of smart transportation and detection mechanism (STDM). This is a combination of the technology in which the application enables the system to detect and automatically read the license id of number plate of vehicle from digitally captured images. Automatically capturing the license plate is the process of detecting and transforming the pixels data of a digital image into the plain text data or ASCII text of the number plate. Our project contains a method for the vehicle number plate recognition from the image using mathematical morphological operations (erosion, dilation). The main objective is to use and combine different morphological operations in such a way that the license plate of the certain vehicle can be detected and translatedeffectively. This is based on various operation such as image improvement, Gray scale transformation, Bilateral Filtering edge detection and getting the number plate from the picture of vehicle. After the completion of the above-mentioned steps, now the process of segmentation is being applied to detect the text present on number plate by making use ofmatching of template and OCR. This system is able to detect the license number accurately as well as quickly from the vehicle’s picture.

Automated number plate recognition system

Automatic Number Plate Recognition (ANPR) system is an automated mass surveillance method that uses several Digital Image Processing (DIP) technique and Optical Character Recognition (OCR) on images to read and identify vehicle registration plates. ANPR has yielded multiple positive results in practical applications such as: access control, traffic law enforcement, inventory and property management, security systems surveillance, parking space allocation, and road traffic surveillance. The automatic number plate recognition system (ANPR) developed in this research work focused mainly on number plate localization and licence plate extraction from an image for possible application in different areas. It achieves this by using several OpenCV digital image processing (DIP) technique developed with python to bring about image segmentation from which some image segments were tested for characters, so that the length of character found on each segment with similar properties becomes the key towards localizing and cropping off the region with the actual vehicle licence plate. Some properties of characters that was used to isolate the possible licence plate are the fact that characters of the licence plate have corresponding image height, width, aspect ratio etc. using these pixel properties it was possible to filter off unwanted contour lines/curves that stands out as noise while localizing the actual region of the image having the plate number. Once the region was obtained OCR was used via a trained template for several character styles to obtain the text format of the licence plate. The work developed had a plate localization accuracy of 100% and 90% read accuracy.

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

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.

Automatic Number Plate Recognition

In this paper an attempt has been made to develop an automatic number plate detection and recognition system for Indian vehicles. The proposed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation and characters are recognized using optical character recognition technique. The system can handle noisy, low illuminated, cross angled, nonstandard font number plates. The morphological transformation, Gaussian smoothing, and Gaussian thresholding the different image processing techniques has been used in the pre-processing stage. The contours have been applied by border following and contours are filtered based on character dimensions and spatial localization for number plate segmentation and then for character recognition K-nearest neighbour algorithm has been used. The proposed system has good accuracy and can be used for e-challan surveillance, stolen vehicle detection and many other applications.

The Study of Plate Number Recognition for Parking Security System

Car Plate Number Recognition System is an important platform that can be used to identify a car vehicle identity. The Recognition System is based on image processing techniques and computer vision. A webcam is used to capture an image of the car plate number from different distance, and the identification is conducted through four processes of stages: Image Acquisition Pre-processing, Extraction, Segmentation, and Character Recognition. The Acquisition Pre-processing stage is extracted the region of interest of the image. The image is captured by live video of the webcam, then converted to grayscale and binary image. The Extraction stage is extracted the plate number characters from binary image using a connected components method. In the Segmentation stage is done by implementing horizontal projection as well as moving average filter. Lastly, in the Character Recognition, is used to identify the segmented characters of the plate number using optical character recognition. The proposed method is worked well for Malaysian's private cars plate number, and can be implemented in car park system to increase level of security of the system by confirming the bar code of the parking ticket and the plate number of the car at the incoming and outgoing gates.

AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

An Automatic Traffic Rules Violation Detection and Number Plate Recognition System for Bangladesh

The traffic controlling system in Bangladesh has not been updated enough with respect to fast improving technology. As a result, traffic rules violation detection and identification of the vehicle has become more difficult as the number of vehicles is increasing day by day. Moreover, controlling traffic is still manual. To solve this problem, the traffic controlling system can be digitalized by a system that consists of two major parts which are traffic rules violation detection and number plate recognition. In this research, these processes are done automatically which is based on machine learning, deep learning, and computer vision technology. Before starting this process, an object on the road is identified through the YOLOv3 algorithm. By using the OpenCV algorithm, traffic rules violation is detected and the vehicle that violated these rules is identified. To recognize the number plate of the vehicle, image acquisition, edge detection, segmentation of characters is done sequentially by using Convolution Neural Network (CNN) in MATLAB background. Among the traffic rules, the following traffic signal is implemented in this research.

Automatic Number Plate Recognition System for the Detection of Unauthorized Vehicles During Government Imposed Lockdowns

Automatic Number Plate Recognition System for the Detection of Unauthorized Vehicles During Government Imposed Lockdowns

Automatic Number Plate Recognition System for the Detection of Unauthorized Vehicles During Government Imposed Lockdowns

Automatic Number Plate Recognition is a precise, error-free technology-integrated system used for the detection and recognition of number plates without human intervention of any sort. Considering the government-imposed lockdown in most of the countries of the world due to the rampant spread of the novel COVID-19 virus, we have attempted to present a fail proof method for the identification of unauthorized vehicles violating the government mandated norms in India without the intervention of police personnel. Furthermore, after the removal of the lockdown this system can be easily extended to detect transgression of traffic laws, automated commercial parking systems etc. The proposed morphological algorithm uses the application of Canny Edge Detector and Connected Component Analysis for the extraction of the plate followed by the usage of Bounding Box morphological technique for the segmentation of the individual characters within the plate. Recognition of the characters is done by Template Matching and Optical Character Recognition System.

Effects of Traffic Calming Measures on Mobility, Road Safety and Pavement Conditions on Abuakwa-Bibiani Highway

Traffic calming measures (TCMs) have been widely adopted as the major speed control devices in Ghana. While initial evaluations have underscored their effectiveness, little research has been conducted to assess their characteristics relative to design guidelines and the negative externalities associated with their misapplications. The objectives of this research were to; i) establish the characteristics of TCMs, and their implications on mobility; ii) to establish essential speed parameters in communities where TCMs are deployed iii) to establish pavement conditions abutting traffic calming measures and; iv) to analyse the accident typology on the Abuakwa-Bibiani road. Method: Firstly, a radar gun and android speed measuring device were used to unobtrusively measure vehicles’ spot speeds and profiles respectively at different locations relative to the TCMs. Also, visual inspections were carried out to assess the pavement conditions surrounding the TCMs while a geodetic laser levelling instrument and a surveying staff were used to obtain the crown heights of the TCMs. Further, the accident typology on the candidate road were analysed to establish how the TCMs are affecting road crashes. Results: Vehicle speeds were generally lower than the posted speed limits in settlements which have TCMs. Nevertheless, the proportion of accident casualties being pedestrians despite lower speeds in settlements remains unacceptably high. Further, the collision typology on this highway were predominantly loss control and rear-end accidents suggesting that the TCMs are playing insidious roles in some of the crash types on this road. Pavement distresses such as depressions, cracks, rutting, ravelling, potholes and deposit of debris were common in the vicinity of the TCMs. Conclusion: Our findings suggest that awareness creation among communities along the highway may be amenable to improving pedestrians’ safety than relying on only engineering countermeasures. The predominant collision types; loss control and rear-end implies that the TCMs are inadvertently contributing to some accident types. For a long term and sustainable speed control, bypassing settlements, use of active dynamic speed bumps (smart or intelligent speed bumps); and a deterrence-based electronic enforcement such as the Automatic Number Plate Recognition System which uses the demerit points offer better alternatives on the highways.

Vehicle Number Plate Recognition System to Identify the Authenticated Owner of Vehicles

Vehicle Number Plate Recognition System to Identify the Authenticated Owner of Vehicles