Vehicle Number Plate Research Articles

Automatic Number Plate Recognition System using Raspberry Pi

Automatic Number Plate Recognition System is an embedded system that acknowledges the vehicle number plate automatically. Automatic Number Plate Recognition is a technology for computer vision to find the number plates of vehicles from the images. There are many applications like parking, access control, security system, etc. In this paper, we propose a technique of implementing Automatic Number Plate Recognition System using Python and Open Computer Vision Library. The different stages that are involved in the implementation are conversion into gray scale, conversion into binary image, detects the edges of the image, to find the contours and finally displays the number plate of a vehicle.

Automated Toll Booth using Morphological Edge Detection Algorithm

In order to reduce the traffic jam, save time and to diminish the money loss of 300 crores/year, an automated system is proposed; The intelligent traffic control management systems which explains the installation of automation in toll plazas, which is a step towards improving the monitoring of vehicles. The main purpose of this arrangement is to implement a system, which automatically identifies an approaching vehicle and records the vehicles number and time. If the vehicle belongs to an authorized person, the toll gate opens automatically and a predetermined amount is deducted from the user account, which leads to reduce the Traffic clogging at toll plazas. The vehicle number plate recognition is done only for authorized members. The proposed model consists of image digitization, edge detection, character detection and recognition with the payment transaction. The proposed method uses morphological edge detection , for the system to identify the character recognition and it uses template matching; it is done for license plates of 6 characters. The license plate character recognition system can be used in vehicle check -in and checkout monitoring system in hotels, malls and can be used to track vehicles on normal roads other than toll booths to avoid thefts.

Pengenalan Plat Kendaraan Bermotor dengan Menggunakan Metode Template Matching dan Deep Belief Network

The license plate of the vehicle is unique and is only owned by one vehicle per vehicle plate series, to make it easier for the police, especially the traffic police, to track traffic violators through the vehicle number plate. The Deep Belief Network algorithm works by processing the dataset through 3 stages, where the first layer is trained, the results of the first layer are then re-trained, and the results of the second layer calculation are made into the third layer count, the mean results on the calculation of the third layer become the result of learning Deep Belief Network then with the Template Matching algorithm, Deep Belief Network is assisted with the introduction of vehicle plates. In a study conducted using the DBN algorithm with the Template Matching method succeeded in recognizing a vehicle plate with a success percentage of 80% from 20 trials. The experiments carried out included plates that were not clearly seen. Failures that occur in the trials are generally due to under- or over-lighting on the vehicle plate.

Automatic Damaged Number Plate Recognition System In Image Processing

The use of the NPR (Number Plate Recognition) is a structure expected to assist confirm the number tags of cars. This structure is anticipated to have the real goal of the security system. This structure is based on an image planning system. This scheme helps to distinguish between the number plates of the cars, to prepare them and to use the information taken care of for further methodology such as securing, empowering the car to pass or to expel the car. NPR is an image scheduling development that utilizes the number (license) plate to acknowledge the car. The goal is to structure a gainful altered attested vehicle unmistakable proof scheme by the use of car number plateThe system is carried out along the route of security control of a particularly restricted area, such as military areas or area around the finest public working settings, such as the Parliament, the Supreme Court, etc. First, the produced system receives a image of the car. The region of the vehicle number plate is evacuated using the picture division in the picture. The optical character affirmation method is used to confirm the character. The resulting information is then used to distinguish and record the information in the database. The structure is performed and impersonated in Python, and the execution is tried on a licensed image. It is seen from the outset that the produced structure acknowledges and sees the car amount vividly.

An Accuracy Examination of OCR Tools

In this research paper, the authors have aimed to do a comparative study of optical character recognition using different open source OCR tools. Optical character recognition (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy

Feasibility of Efficient Number Plate Recognition using Morphological Dilation and Support Vector Machine

Intelligent data acquisition of vehicle number plate plays a significant role to recognize a vehicle and it automatic parking, traffic movement and scheduling, tracking of stolen vehicle, and many more. Although different methodologies of automatic number plate reading have developed alongwith their algorithms, still an efficient number plate recognition technique for better segmentation and recognition of the captured number plate using Morphological Dilation and Support Vector Machine (SVM) are expected to be helpful. In this paper, we present a modified method for recognition of contents of number plate using morphological dilation and SVM. We have compared our results with those from the existing models using Wavelet Transform and Artificial Neural Network techniques. Superiority of present methodology is established using parameters like image segmentation and recognition.

Intelligent Vehicular Flow Management System for Educational Hub

In rural and urban arenas, quantity of vehicle is expanding. To find parking area is turning into a primary concern. To design parking framework which is able to neglect physical efforts and also provide arena for vehicles parking. In later, instructive grounds are expanding which consist of Universities, designing, Art, Commerce and Science Colleges. The issue is parking spots either inadequate as indicated by the requests of understudies and staff or these spaces are ineffectively overseen. With each new individual entering college premises have an issue on grounds. In this method, we present the idea of the shrewd vehicle flow management. Here we will convey an Internet of Things (IoT) based framework which is utilized to detect the nearness and development of vehicles. Here, we have considered example of our V.I.I.T college campus to develop prototype which has 5 openings for each division. The vehicle flow management parking framework is creating in 2 stages. In the principal stage, the information, for example, vehicles utilized, offices, and so on about the educator and staff data will be gathered and information is grouped according to office. In the second stage, the observation camera is catching pictures of the vehicle. By utilizing a novel methodology like Quadrature lock-in Discrimination (QLD), decreasing poor visibility because of stray light or mist to peruse the vehicle plate number. For the majority of part, in existing propelled frameworks doesn’t give office shrewd separate stopping framework. We proposed a framework which has diverse vehicle flow management framework for various office and understudy/educator just from the office can stop in apportioned spaces. The framework utilizes Raspberry Pi has confederated with the Radio Frequency Identification (RFID) per user, associated with a website page by means of its integrated Wi-Fi. RFID tags are distributed to college members and slot allocation is given dynamically by system.

Designing Automatic Number Plate Recognition (ANPR) Systems Based on K-NN Machine Learning on the Raspberry Pi Embedded System

Research on vehicle number plate recognition or Automatic Number Plate Recognition (ANPR) is mostly done by researchers to produce an introduction that has high accuracy. Several methods of introduction are carried out such as introduction to edge detection and morphology, relationship analysis between objects, machine learning and deep learning. In this research a K-NN machine learning ANPR system was developed in character recognition. The method of analyzing relationships between objects is used to localize number plates. The system that was developed also added an artificial intelligence to be able to find out the fault of the number plate recognition and fix it based on the position of the character group in the number plate. The ANPR system is designed to be an Embedded system so that it can be implemented to be able to carry out the identification of two-wheeled and four-wheeled vehicle license plates. The ANPR system was also developed to be used in the parking management system. In this research the recognized number plates are limited to private number plates in Indonesia. In testing, the system is made capable of recognizing the number plates of two-wheeled vehicles and four-wheeled vehicles on vehicles that have a standard license plate according to Polri regulations, both in the font type and the number plate writing format. The results of vehicle number plate recognition reached an accuracy of 98%.

Design and Analysis of Car Parking Slot Indicator

Present days vehicle leaving has turned into a noteworthy issue in urban zones with absence of leaving offices and expanded measure of vehicles, because of this drivers who are scanning for parking spot they were wandering around the city in pinnacle hours. This causes traffic, exercise in futility and cash. To keep away from every one of these issues an outcome is created utilizing sensor circuit, RFID and GSM. RFID is utilized to identify the vehicle subtleties, IR sensor is utilized to discover the nearness of the vehicle and all subtleties are gotten to remotely through GSM. This framework encourages driver to discover parking spot accessibility with the assistance of GSM innovation by giving leaving free space data and keeps up the database of the left vehicles. Drivers can check whether the openings are free for stopping ahead of time by sending code as sms to a number which is accessible in the site of that specific shopping centers, emergency clinics, theaters and so forth., at that point drivers get an answer as sms contains parking spot accessibility data. In the event that driver need to book the uninhibitedly accessible space he can book by sending another code which is accessible in the site as sms. Notwithstanding the stopping there are three highlights included, they are Theft the board likewise included for example at the point when vehicles want leaving then every single vehicle number plate is checked with burglary list in the database utilizing RFID, on the off chance that it is in robbery list, at that point a message is sent to the administration. At the point when fire mishap happens all of a sudden because of any causes like short out and so on, naturally water will be sprinkled with the goal that harm of the vehicles can be maintained a strategic distance from. As per the power of light the lights will be ON or OFF inside the stopping openings. This spares control utilization. This model created for the stopping framework with less human collaboration, expands adaptability.

License Number Plate Recognition using Template Matching and Bounding Box Method

Recognition of vehicle number plates is very important in the field of transportation security systems. Template matching is one of the most popular techniques used for number plate recognition. This algorithm works by evaluating the image pattern that will be recognized and then comparing it with the image pattern in the template. A number plate is an object in an image that has many characters which usually consists of numbers and letters. Character recognition cannot be done simultaneously in an image. A number plate object in the image needs to be done early processing, normalization, and segmentation to recognize the character. While to separate each character, a bounding box algorithm is used. This technique is relatively simple and has fast computing. So this study examines a combination of template matching and bounding box methods to detect Indonesian car license plates. Based on the tests that have been done, the accuracy reaches 80%.

Vehicle plate number localization using a modified GrabCut algorithm

Vehicle plate number recognition plays an important role in traffic control and surveillance systems. A key stage in any vehicle plate number recognition system is to first locate the vehicle plate number. In this paper, we present a modified GrabCut algorithm for localizing vehicle plate numbers. In contrast with the traditional interactive GrabCut technique, a modified GrabCut algorithm was designed to identify and extract vehicle plate numbers in a completely automatic manner. Our approach extends the use of the traditional GrabCut algorithm with addition of a feature extraction method which uses geometric information to give accurate foreground extraction. Finally, to evaluate the performance of the proposed technique, the localization accuracy is tested with a dataset of 500 vehicle images with vehicle plates from different countries. An accuracy of 99.8% was achieved for the localization of vehicle plates. Comparative analysis is also reported.

Automated parking management system for identifying vehicle number plate

<p>By using image processing, the Automated parking management system (APMS) to recognize the license plate number for efficient management of vehicle parking and vehicle billing. It is an independent real-time system, reduces number of people involvement in parking areas. The main aim of this system is to automated payment collection. This (APMS) system extract and recognize license plate numbers from the vehicles, then that image is being processed and used to generate an electronic bill. Generally in the parking lots heavy labor work is needed. This system used to decrease the cost of the labor and also enhance the performance of the APMS. This system is composed of vehicles license plate number extraction, character segmentation and character recognition. A proper pre-processing is done before extracting the license plate and it also generates the entry time and exit time of the vehicle and finally generates the electronic bill.</p>

Development of a Two Factor Authentication for Vehicle Parking Space Control based on Automatic Number Plate Recognition and Radio Frequency Identification

This paper proposed a two factor authentication for vehicle access controls using Automatic Number Plate Recognition (ANPR) and Radio Frequency Identification system (RFID) for the University of Zambia (UNZA) vehicle access points. The University of Zambia is experiencing increasing challenge of car parking space and vehicle access controls to and within campus premises. The survey that was conducted reviewed that members of staff found difficulties finding parking spaces due to intrusion. The survey also reviewed that vehicles have been stolen within campus parking areas without detection. An access control system using integrated ANPR and RFID technologies was developed to provide five authentication states that met different vehicle access point’s requirement. It was built with ‘ORed’ and ‘ANDed’, logic settings to achieve five different states of authentication levels, each suited for a particular access point. The ANRP system used the vehicle number plate to authenticate the vehicle through the use of the camera. On the other hand, the RFID system used the drivers’ card/tag through the RFID card reader to authenticate the user. Daily transaction records were sent to the security center where information would easily be retrieved. Illegal access to restricted areas, threats of theft of motor vehicles and failed transaction recording system was amicably solved by this proposal.

Implementasi Sidik Jari sebagai Otentikasi Parkir Kendaraan Menggunakan Raspberry Pi

Otentikasi keluar masuk kendaraan pada tempat parkir merupakan salah satu permasalahan yang masih perlu dieksplorasi. Sebagian besar tempat parkir menggunakan otentifikasi mencatat nomor kendaraan pada kartu parkir. Petugas memverifikasi nomor kendaraan yang tertera dikartu parkir dengan nomor kendaraan yang tertera pada Surat Tanda Nomor Kendaraan (STNK). Metode otentifikasi tersebut membutuhkan ketelitian dari petugas parkir. Penelitian yang dilakukan memanfaatkan identifikasi sidik jari pengemudi kendaraan dan pengenalan plat nomor kendaraan melalui foto oleh petugas parkir sebagai otentikasi. Perekaman sidik jari dan foto kendaraan dilakukan pada saat kendaraan masuk. Identifikasi ini digunakan pada saat kendaraan akan keluar. Sistem dibangun dari beberapa perangkat, yaitu sensor ultrasonic untuk mendeteksi keberadaan kendaraan. Sensor fingerprint untuk mendeteksi sidik jari yang dilengkapi dengan storage untuk menyimpan template sidik jari. Webcam untuk pengambilan foto kendaraan sebagai verifikasi pada saat kendaraan keluar tempat parkir. Sedangkan pintu portal digerakan oleh motor servo. Semua sistem dikendalikan menggunakan Raspberry Pi 3 menggunakan bahasa pemprogram python. Hasil pengujian pada sensor ultrasonic mampu mendeteksi keberadaan kendaraan pada jarak 10-200 cm dengan tingkat keakurasian 96%. Motor servo berputar posisi 0° jika nilai DutyCycle diatur 2,5 dan berputar 90° jika nilai DutyCycle 7,5. Posisi webcam berada 2 meter dari letak sensor fingerprint. Posisi jari yang ditekan menghasil proses yang lebih cepat dalam mengidentifiaksi karakter sidik jari pada saat perekaman dan pencocokan sidik jari.

Comparasi Edge Detection Roberts dan Morfologi Pada Deteksi Plat Nomor Kendaraan Roda Dua

Refers to the difficulty factor on the detection plate on this research will focus on the detection of two-wheeled vehicle number plate, where the data will be taken from the farm garage. The resolution of this problem will be attempted using techniques of digital image processing method of detection of edge (edge detection) and morphology. Study on the evaluation and the research results will be calculated using the technique of confussion matrix, this technique will measure on the success rate of approach will be obtained from the proposed method. of the proposed method is sufficiently high, namely 53% success rate, while the value of positive predictive value by which this value to know the success rate of the method to detect the entire image test is still extremely less just reached 40% success, while the value of negative value which is the value of preditive to know separation detection noise his success pretty well with a value of 56% success rate Keyword: Edge Detection, Morfologi, Confusion Matrix

AUTOMATION OF VEHICLE PLATE NUMBERS IDENTIFICATION ON ONE-ASPECT IMAGES

The subject matter of the article is the method of automating the identification of vehicle plate numbers based on the processing of one-aspect images obtained using video recording means. The goal is to provide automation of the process of identifying vehicle plate numbers within a wide range of changing the viewing angles and the levels of illumination. The task is formulation of the method of automated identification of vehicle plate numbers on one-aspect images, which are obtained by means of video fixation within wide limits of changing both the viewing angles and the levels of illumination. Analysis of the problems of methods and algorithms of automated detection and recognition of vehicle plate numbers has shown that it is most promising to use flexible algorithms that adapt to the changing conditions of observation of traffic control devices. One of the promising technologies for implementing such algorithms is the application of artificial neural networks. The solution of the problem of recognition of vehicle plate numbers can be represented as a complex of image processing and analysis of algorithms, which includes the initial preparation of the image, the discovery of the area of the vehicle plate on the image, the segmentation of symbols and the recognition of symbols. Conclusions: an algorithmically implemented method of identifying vehicle plate numbers, which makes possible searching the text areas under an arbitrary angle in different lighting conditions, is proposed. This method allows automating the process of identification of vehicle plate numbers within a wide range of distances to the car, as well as viewing the angles and levels of illumination. The purpose of further research is to improve the proposed method for its implementation, using modern software and hardware.

Application independent localisation of vehicle plate number using multi‐window‐size binarisation and semi‐hybrid genetic algorithm

This study introduces an enhanced version of a previously published genetic algorithm based technique to allow fast and accurate detection of the vehicle plate number independently of the used application. Hence, significant enhancements are introduced to upgrade the genetic algorithm into a semi-hybrid category by preceding it with a sorting and subgrouping process that reduces the search space and following it by a rule-based local search to optimize its output. The updated population structure and its initiation with subsets of the sorted image foreground objects results in a linear relationship between the image complexity represented by the number of foreground objects and the genetic algorithm search space instead of being exponential in previous versions. Moreover, a novel extra variable-size-window adaptive binarisation step is introduced to overcome the problems of attached license symbols that cannot be solved by the skipping ability introduced in previous versions. Various image samples with a wide range in scale, orientation, and symbol connectivity have been experimented to verify the effects of the new improvements. Encouraging results with 99.2% detection accuracy have been reported using a public dataset outperforming the author of the dataset by more than 5.5% and the state of the art systems by 2%.

Study on Single Camera Based ANPR System for Improvement of Vehicle Number Plate Recognition on Multi-lane Roads

In this paper, we introduce the single camera-based number recognition system used for this system recognizes vehicle number plates on one lane by using a single camera. Intelligent transport system (ITS) has been constructed because there is a limit in solving traffic problems in a physical manner such as construction of roads and subways. The single camera-based number recognition system used for this system recognizes vehicle number plates on one lane by using a single camera. Due to the increased cost of the installation and maintenance thereof, there is a growing need for a multi-lane-based number recognition system. When the single camera-based number recognition system is used for multi-lane recognition, the recognition rate is lowered due to a difference in vehicle image size among lanes and a low-resolution problem. Therefore, in this study, we applied a character extraction algorithm using connected vertical and horizontal edge segments-based labeling to improve multi-lane vehicle number recognition rate and thereby to allow application of the single camera-based system to multi-lane roads.

Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques

Vehicle number plate recognition (VNPR) system is a digital image processing techniques which is broadly used in vehicle transportation system to identify the vehicle by their number plate. Yet it’s a very challenging problem, due to the diversity of plate formats, different scales, and non-uniform illumination conditions during image acquisition. This research mainly focuses on Nepali vehicle number plate recognition system in which the vehicle plate image is received by the digital cameras and the image was then processed to obtain the number plate information. A real image of a vehicle is captured and processed using various algorithms. Morphological operations, and edge detection, smoothing, filtering, techniques for plate localization and characters segmentation for segment character and these segmented character was cut in to block of 70×70 size and calculate the correlation with the template of database using the template matching algorithm normalized cross correlation and phase correlation and compare this result in term of accuracy. The system was tested by 90 patterns under several conditions. It includes experiment of number plate recognition using phase correlation and normalized cross correlation methods. From the study and analysis of test after applying on number of images of database, the normalized cross correlation method was found more accurate to recognize the number plate then phase correlation method and recognition accuracy of normalized cross correlation was 67.98% and phase correlation was 63.46%.

Performance Evaluation of Automatic Number Plate Recognition on Android Smartphone Platform

<span>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.</span>