Number Plate Recognition System Research Articles

Enhancing ANPR accuracy for staff car verification using convolutional neural networks and RegEX correction

This research paper presents the development and evaluation of an Automatic Number Plate Recognition (ANPR) system using Convolutional Neural Networks (CNN) with Regex correction. The aim is to enhance the accuracy and effectiveness of car verification and security processes at First Technical University, Ibadan. The ANPR system was implemented both without Regex correction and with Regex correction. The evaluation results demonstrate significant improvements in the system's performance when CNN with Regex correction is employed. The CNN-based ANPR system achieves a precision of 1.00, recall of 0.90, and F1-score of 0.95 in accurately identifying number plates. These scores indicate increased accuracy and reduce false positives compared to the system without Regex correction. The integration of CNN and Regex correction effectively handles variations and errors in the number plate data, leading to a reliable and efficient car verification process. Future work can focus on further refining the CNN model and optimizing the Regex correction algorithms to enhance the system's accuracy and robustness. The developed ANPR system, utilizing CNN with Regex correction, shows great potential for enhancing car verification and security in various domains, including law enforcement, parking management, and traffic monitoring

Vehicle Number Plate Recognition

Recognizing vehicle number plates is a difficult but much needed system. This is very useful for automating toll booths, automated signal breakers identification, finding out traffic rule breakers and to detect stolen vehicles. The developed system consist of a Raspberry Pi based vehicle number plate recognition system that automatically recognizes vehicle’s number plate using image processing. The system uses a digital camera interfaced to a Raspberry pi processor. A rear image of vehicle is captured and processed using various algorithms. The system constantly processes incoming camera footage to detect any trace of number plates. On sensing a number plate in front of the camera, it processes the camera input and extracts the number plate part from the image. The image is extracted using OCR. And by using CNN techniques, the number is extracted from the image. The system then displays the extracted number. The implemented system consist of a fully functional vehicle number plate recognition system using Raspberry Pi considering success rate and processing time as parameters. The developed system can be used for security purpose in housing societies to monitor the entry/exit of authorized vehicles. It is observed that the developed system successfully detects and recognizes the vehicle number plate on real time images. The system accuracy is about 80%

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Comparison Study About Vehicle Detection and Plate Number Recognition

Vehicle detection is crucial to the management of Intelligent Transportation Systems of urban traffic. Optical character recognition is many systems use, like the automatic number plate recognition system (NPR). Using various techniques and methodologies, such as convolution or deep neural networks, morphological operations, optical character recognition, and edge detection, such a system has been widely utilized to identify car license plates. Numerous applications algorithms of vehicle identification become possible after the development of many helpful techniques in computer vision and the availability number of related images as a dataset. In this paper, the various plate numbers of the specific vehicle will be and reviewed. Vehicle detection systems suffer from a variety of difficulties and constraints such as the all the surrounding state of clarity for the license plate, non-standard formats of the plate, complex situations, camera quality and clarity, the position and distance of the camera, the distortion in the image, contrast issues, reflections and the state day and night images As a result, numerous methods for comprehending and assessing the idea behind the vehicle number plate recognition systems have been presented with simply explaining the importance and drawbacks of each system.

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this article, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, U.K. By developing a two-stage graph deep learning model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error of only 11.18 and a root mean square error of 19.49; most importantly, the overall system resilience is maintained at 70% in the meantime. This article provides an alternative perspective for dealing with the well-known “efficiency-resilience” dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine

Automatic Number Plate Recognition (ANPR) is an important tool in the Intelligent Transport System (ITS). Plate features can be used to provide the identification of any vehicle as they help ensure effective law enforcement and security. However, this is a challenging problem, because of the diversity of plate formats, different scales, rotations and non-uniform illumination and other conditions during image acquisition. This work aims to design and implement an ANPR system specified for Yemeni vehicle plates. The proposed system involves several steps to detect, segment, and recognize Yemeni vehicle plate numbers. First, a dataset of images is manually collected. Then, the collected images undergo preprocessing, followed by plate extraction, digit segmentation, and feature extraction. Finally, the plate numbers are identified using Support Vector Machine (SVM). When designing the proposed system, all possible conditions that could affect the efficiency of the system were considered. The experimental results showed that the proposed system achieved 96.98% and 99.19% of the training and testing success rates respectively.

LOKALISASI PLAT NOMOR DENGAN JAVA MENGGUNAKAN DETEKSI TEPI CANNY DAN FILTER MORFOLOGI

This study aims to introduce an automatic vehicle identification system based on vehicle licenses and license plate recognition. This Automatic Number Plate Recognition (ANPR) system software uses a series of image processing algorithms captured by a camera with a high degree of accuracy and has the efficiency to recognize license plates and then identify vehicles from datasets stored on a Personal Computer (PC). This study uses an algorithm for license plate localization by implementing the Canny edge detection algorithm and a morphological filter.The methodology used is to develop a Number Plate (NP) localization method including the following steps: (1) Color conversion, image enhancement (using sharpness filters and gamma correction), Canny edge detection and alignment (skeletonization); stage (2) morphological filter-based method to determine the Region of Interest (ROI), namely a rectangular box containing a Number Plate (NP). A rectangular bounding box is drawn across the area in the original image that contains the NP after the NP are localized. The bounding box is equal to the width of the image plus one pixel and the height of the NP plus one pixel.The results showed that this method succeeded in achieving a localization rate of about 72.0%. The method used for license plate localization is able to localize license plates with clear boundaries and unambiguous separation of other related components. This research can be further developed to include more functions so that it can be used by all elements of society from researchers to law enforcers.

The Intelligent Vehicle Number Plate Recognition System based on Arduino

Abstract: The thesis proposes the development of The Intelligent Vehicle Number Plate Recognition System (IVNPRS) using the Arduino platform. The system uses image processing techniques to automatically detect and recognize vehicle number plates, and it includes a camera module, an image processing unit, and an Arduino microcontroller. The proposed system has practical applications such as security monitoring, automatic toll collection, and traffic monitoring. The thesis discusses the different components of the system, image processing techniques used, and evaluates its performance by measuring accuracy, speed, and robustness under different lighting and weather conditions. The experimental results suggest that the VNPRS has the potential to improve vehicular traffic and safety in various locations.

Image Enhancement and Automated Number Plate Recognition

Automatic Number Plate Recognition (ANPR) and Vehicle Number plate detection (VNPD) systems. ANPR, is a technology that enables automatic detection, recognition, and identification of vehicle license plates, while VNPD is a subset of ANPR that focuses specifically on detecting and recognizing license plates.Several researchers have explored different approaches to ANPR and VNPD systems, as evidenced by the various papers listed. For instance, presented an e security system for vehicles number tracking at a parking lot, proposed a method of monitoring traffic signals, and violations using ANPR and GSM. In order to proceed, the conventional Grab Cut algorithm must first interactively give a candidate frame. for the target detection job to be done.To automate the identification of the licence plate by the Grab Cut algorithm, we update the candidate frame by incorporating the aspect ratio of the licence plate as the foreground extraction feature. Then, to fully implement picture noise reduction, we combined the Bernsen algorithm with the Wiener filter, which is extensively used in the fields of digital signal processing in order to increase the detection precision of conventional target identification techniques. Overall, the papers listed in the question demonstrate the wide-ranging applications of ANPR and VNPD technologies, from parking lot security to traffic signal control to driver assistance systems. These technologies have the potential to improve safety, efficiency, and security on the road, and researchers continue to explore new approaches to their development and implementation. Keywords: Detection of Number Plate, Convolutional Neural Network-(CNN), Object detection, character identification, Machine Learning-(ML).

Vehicle Number Plate Detection

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The resulting data is then stored in a database along with the time-stamp. The system is implemented and simulated in python, and its performance is tested on real image.

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

Automatic vehicle number plate recognition system

In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

Vehicle Number Plate Recognition Using Raspberry Pi

Vehicle number plate recognition (VNPR) systems have gained significant attention due to their wide range of applications in traffic management, parking enforcement, and security systems. This abstract presents an overview of a VNPR system that utilizes computer vision techniques and machine learning algorithms for license plate detection and recognition. The system is implemented using a Raspberry Pi platform, which provides a cost-effective and portable solution. The system follows a systematic approach to perform license plate recognition. Firstly, the camera captures images or video frames, which are then processed using the OpenCV library to enhance the visibility and quality of the license plates. License plate detection algorithms, employing edge detection or machine learning-based object detection techniques, are applied to locate and extract the license plate regions in the processed images. Following license plate detection, character segmentation techniques are utilized to isolate individual characters on the plate. Finally, integrated into the system, the Tesseract OCR engine recognizes the segmented characters and extracts the text information, effectively providing the license plate data.

A Review on Location Based Efficient Road Toll Collection System using RFID and NPRS Techniques

The road traffic controlling and road toll collections are became one of the challenging tasks in most of the developing and developed countries. The tollgate systems doesn’t account and verify from which particular place (source) the vehicle is started or joined that concern toll road and up to which particular point (destination) it reached/left/disjoined/used the road. If a toll road is of 60 KM length and a vehicle X started 05 KM away from its tollgate and another same category vehicle Y starts from 60 KM away will have to pay same charges. These existing systems are unable to tell which vehicles are using how much road services and accordingly how much should they pay for it. Therefore, specifically in Indian state highways are concern, road toll collection systems are not efficient and not justifying with the road pricing practices with vehicle owners. Due to these, most of the vehicle owners and passengers are facing numerous types of problems while travelling from one place to another place by road. To solve these issues, I tried to propose an efficient location based road toll collection system using RFID (Radio Frequency Identification) and NPRS (Number Plate Recognition System) techniques in this paper. The proposed model will help to collect toll charges efficiently from all vehicle owners as per their road usage.

Various License Plate Detection and Recognition Methods using Computer Vision and Machine Learning

With the increasing advancements in the technology, our lives have become significantly more convenient. We now have automated many things. One example of such things is the automated number plate recognition system. There are many ways to perform the ANPR (Automatic Number Plate Recognition). Performing ANPR in wild still remains a big challenge. This review focuses on some techniques that have tried to overcome this challenge.

Design and Implementation of an Intelligent Parking System (ISPS) With Laser Security System (LSS)

      In order to solve one of the most important problems, especially in a large and crowded city like Baghdad, is finding an empty and secure car parking space.  In this work, an intelligent Smart Parking System (ISPS) has been investigated and developed by providing information about the empty lots and helping the driver to get to his parking spot with a rotary parking system to reduce the traffic in the parking area. The ISPS Park implements a car number plate recognition system as a security system for car identification. Moreover, for increased security, a fence with a Fiber-Optic vibration system has also been used to prevent any object from entering a parking space without a license. The ISPS was run with the pre-programmed controller to make minimum human involvement in the parking system to ensure access control in restricted places. Matlab with Arduino tools has been used for the car plate number recognition process and car entering control. The system shows that using RFID for entering and leaving is so fast and informative. A Fiber SenSys program has been used with the Alarm Processing Unit (APU) to prevent any unlicensed person from trying to cut, climb or crawl under a fence.

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.

Automatic Number Plate Recognition System (ANPR)

Object detection and Image processing are combinedly vast area of research. Various Notable works have been done in the field of transportation in order to ensure a healthy and lawful traffic environment. A precise and quick vehicle number plate recognition system is a solution to traffic control and resultant in improved regulations. This paper is based on optical character recognition which uses improved techniques and ensures optimized results. A full fledged algorithm of number plate recognition is proposed and the same acquires upgraded accuracy over existing system. The entire system can be mounted as a solution traffics issues related to law and security. Automatic Number Plate Recognition (ANPR) system consists integration of machine learning together with image processing and pattern identification. Optical character recognition (OCR) is an algorithm that plays an important role in automatic number plate recognition and it is one of the main aspect of Research in machine learning and computer vision that has evolved immensely since its inception.

Automatic Vehicle Number Plate Recognition Approach Using Color Detection Technique

An Automatic Vehicle Number Plate Recognition System (AVNPR) is a key research area in image processing. Various techniques are developed and tested by researchers to improve the detection and recognition rate of AVNPR system but faced problems due to issues such as variation in format, lighting conditions, scales, and colors of number plates in different countries or states or even provinces of a country. Douglas Peucker Algorithm for shape approximation has been used in this research to detect the rectangular contours and the most prominent rectangular contour is extracted as a number plate (NP) and the connected component analysis is used to segment the characters followed by optical character recognition (OCR) to recognize the number plate characters. A custom dataset of 210 vehicle images with different colors at various distances and lighting conditions was used for the proposed method captured on my smart phone Galaxy J7 Model SM-j700F at roads and parking. The dataset contains various types of vehicles (i.e. Trucks, motorcars, mini-buses, tractors, pick-ups etc). The proposed method shows an average result of 95.5%. The novelty used in this method is that it works for different colors simultaneously because in Pakistan, several colors are used for vehicle NPs.

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

The number plate recognition system must be able to quickly and accurately identify the plate in both low and noisy lighting conditions, as well as within the specified time limit. This study proposes automated authentication, which would minimize security and individual workload while eliminating the requirement for human credential verification. The four processes that follow the acquisition of an image are pre-processing, number plate localization, character segmentation, and character identification. A human error during the affirmation and the enrolling process is a distinct possibility since this is a manual approach. Personnel at the selected location may find it difficult and time-consuming to register and compose information manually. Due to the printed edition design, it is impossible to communicate the information. Character segmentation breaks down the number plate region into individual characters, and character recognition detects the optical characters. Our approach was tested using genuine license plate images under various environmental circumstances and achieved overall recognition accuracy of 91.54% with a single license plate in an average duration of 2.63 seconds.