Vehicle Number Plate Recognition Research Articles

Modified ANPR using Neural Networks

Number Plate Recognition is a mass observation technique which is used to identify the vehicles. The identification and acknowledgement of a vehicle license plate is a key method in the greater part of applications related to vehicle movement. Moreover, it is a very famous and dynamic research subject in the field of image processing. Since every vehicle have a unique plate number, so if we have to perceive a specific vehicle we can utilize the license plate. The main objective of automatic vehicle number plate recognition is to design an efficient automatic authorized vehicle identification system by using the number plate. It has three modules namely license plate extraction, segmentation and recognition. . Different methods, techniques and algorithms have been developed to detect and recognize license plates. Nevertheless, due to the license plate characteristics that vary from one country to another in terms of numbering system, colours, language of characters, fonts and size. Further investigations are still needed in this field in order to make the detection and recognition process very efficient. Although this domain has been covered by a lot of researchers, various existing systems operate under well-defined and controlled conditions. For example, some frameworks require complicated hardware to make good quality images or capture images from vehicles with very slow speed. For this reason the detection and recognition of number plates in different conditions and under several climatic variations remains always difficult to realize with good results. For that, we present an automatic system for number plate detection and recognition based on convolutional neural networks. CNN has proved its robustness even with distorted, tilted and illuminated datasets.

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

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

Proposal for Automatic Vehicle Number Plate Recognition System in POLIMAS

Automatic Vehicle Number Plate Recognition (AVNPR) system is an image processing technology in computer vision which captures the image of the vehicle and recognizes its number plate. Currently, the security and management of transportation system becomes an important key in controlled place such as campus area. With an increased number of vehicles, there is a need for vehicle recognition mechanism that is effective, affordable and efficient. The objective of this paper is to propose an efficient automatic vehicle identification system by recognizing the vehicle plate number. The system will be installed at the main entrance of POLIMAS to ensure that only the authorized vehicles can automatically enter the campus area. Once the vehicle is detected by the input sensors, AVNPR system will capture the image of vehicle plate number. An image is then extracted and investigated character segmentation by using optical character recognition (OCR). The method used for detection of a plate number is by pre-processing of the image and using a combination of Sobel edge detection and Laplacian edge detection techniques. Bounding Box technique is used to find the number plate and character segmentation. For character recognition, OCR method is used by using template matching to compare the segmented image with the template. The system is sustainable as the camera will only be switched on when a car is present. The propose system successfully detects and recognizes the vehicle number plate on real images. This system can also be used for security and traffic control.

Indonesian License Plate Detection Using Morphological Operation

Technological developments have enabled the use of vehicle number plate recognition in various fields such as parking systems, e-toll payments, handling motor vehicle theft cases, traffic violations and so on. Vehicle number plate characteristics in each country have characteristics that are not the same. Each country has a standard numbering system in its writing method, colour, language and size of the license plate, so that to take the research standards for license plate numbers from other countries is not effective. Special research is needed in accordance with the format of the number plate used by the country, in this case Indonesia. This research applies simple method, morphological operations in detecting vehicle plates in Indonesia. Morphological results will be segmented to get the image of the plate character separately. The test results show that the method applied can provide a maximum accuracy of up to 100% on taking plate images with a distance of 1.5 meters.

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.

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%.

Vehicle Number Plate Detection and Recognition of Characters by Image Processing

Vehicle Number Plate Detection and Recognition of Characters by Image Processing

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%.

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.

Design and Implementation of a Vehicle License Plate Characters Recognition System based on FFBN Classifier

In this paper, a coercive and dynamic Automatic Vehicle Number Plate Recognition (AVNPR) scheme is obtained, a throng supervision structure able to detect Indian license plates; it is a cardinal procedure in Intelligent Transportation Systems (ITS). The progression of detection and identification of License Plates in this proposed system is divided in four sections: Image Pre-processing, License Plate Localization, Character Segmentation and Character Recognition. A Feed Forward Back-propagation Neural network (FFBN) Classifier is engaged for this particular system for License Plate exposure and features mining of License Plate characters. The outcomes demonstrate that the proposed system can successfully distinguish and identify License Plates even in problematical surroundings. A superior entitlement of accurateness has been received for the implication of this method and it is confirmed to be 92.13% for the withdrawal of License Plate area and 90.55% identifications of License Plate characters with greater concert than conventional approaches. The planned structure has been put into practice in MATLAB.

Highly Adaptive Indian High Security Vehicle Number Plate Recognition

The recognition techniques are very useful to build the automatic systems, especially it make electronic systems alive as an artificial intelligence devices. The recognition adds functionality of the existing working devices, which enhances the precision as well as reduces manpower, human errors and increases the speed of work. The recognition has many branches like face recognition, object recognition, colour recognition, character recognition etc. In this paper an algorithm is developed to recognize the Indian Vehicle High Security Number Plate with optimum accuracy. The whole recognition procedure has feature database preparation as well as recognition process. The accuracy of the proposed system model is quite high as compared to existing methodologies. The system is very simple and fast to work.

NUMBER PLATE RECOGNITION USING OCR TECHNIQUE

Automatic Number Plate Recognition (ANPR) is a spec ial form of Optical Character Recognition (OCR). AN PR is an image processing technology which identifies the vehicle from its number plate automatically by digital pict ures. In this paper we have presented an algorithm for vehicle number identific ation based on Optical Character Recognition (OCR). OCR is used to recognize an optically processed printed character number pla te which is based on template matching. This algori thm is tested on different ambient illumination vehicle images. OCR is the las t stage in vehicle number plate recognition . In recognition stage the characters on the number plate are converted into texts. The char acters are then recognized using the template match ing algorithm. Index Terms : Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Template Matching

Video Analytic Algorithm for License Plate Recognition System

Security has always been the most dominant factor in all domains of everyday life. Companies are spending more and more in this domain as security have become an international issue especially after war on terror era. Not only national departments have become victim of it but general public has also suffered a huge deal due to the lack of security measures in the recent past. This is one of the main reasonfor investment in this domain. From traffic monitoring and security purposes, a vehicle number plate recognition system can play a very useful role in monitoring the vehicle's movement and consequently providing information about the vehicle. Different countries have their own system of issuing number plates. Similarly different algorithms are designed for number plate recognition in different countries. The proposed system constitutes an algorithm which is designed for the vehicles residing in Pakistan. Digital image processing techniques are the basis of the proposed system including the image enhancement and filtering techniques for noise and other weather effects reduction. Hough transform is used to segment the characters and consequently recognize the character. A very large data set has been used to test the system which shows quite immaculate results.

Number Plate Recognition Based on Multi Feature and Character Recognition

Intelligent transportation system has become an integral part of the transportation system and it consists of number plate recognition. A recognition system implemented to control the payment of fees in parking areas, highways, airport or harbour cargo control etc. There are considered an approach to identify the vehicle through recognizing of its license plate using multi feature recognition and neural network techniques. Multi feature recognition is to enhance several features of for target. The proposed method use improved character recognition by through setting proper numbers of hidden layers in the character kinds. This method can improve the efficiency and accuracy of vehicle number plate recognition. Multi pattern recognition is an algorithm for recognition of multi feature concept into the information. Proposed method solves the number plate with colour features as well as texture features and back ground features. By using multi feature concept problem of the ambiguity of character recognition can be solved. After using multi feature concept for number plate positioning, it can be obtained the projection of histogram mainly through a threshold. The result of number plate recognition is to change the plate character in to text character. Using back propagation of neural networks to resolve complex characters number plate and defective characters, and avoid heavy data analysis and mathematical modelling works. Recognition problems of automobile number plate such as character kinds, the numbers, and confusing of words can also be solved Keywords: fuzzy theory; number plate recognition; character recognition.

Identifikasi Plat Nomor Kendaraan Secara Off-Line Berbasis Pengolahan Citra Dan Jaringan Syaraf Tiruan

Vehicle number plate identification and recognition systems used in many aplications, such as parking lot access, traffic control, and vehicles monitoring and security. In this research, an offline system to read and identify vehicle number plate is developed. Vehicle number plate identification process is performed in two steps, the first is the character image processing and the second is the pattern recognition with artificial neural networks. The character image processing consists of binerization, open-close morphology, median filtering, slicing and resizing. Backpropagation algorithm is performed to train the neural networks, starting from the output layer to the input layer with output error is used to change network values. Testing is carried out in two scenarios, The first one is with the image samples which are included in the training step. The second one is with the image samples which are not included in the training step. The testing results show that the system is able to identify as much as 96% and 90% of number plate image samples for each scenarios.

Automatic Parking Management System and Parking Fee Collection Based on Number Plate Recognition

This paper discussed on automatic parking system and electronic parking fee collection based on vehicle number plate recognition. The aim of this research is to develop and implement an automatic parking system that will increase convenience and security of the public parking lot as well as collecting parking fee without hassles of using magnetic card. The auto parking system will able to have less interaction of humans and use no magnetic card and its devices. In additions to that, it has parking guidance system that can show and guide user towards a parking space. The system used image processing of recognizing number plates for operation of parking and billing system. Overall, the systems run with pre-programmed controller to make minimum human involvement in parking system and ensure access control in restricted places. This paper presents algorithm technology based method for license plate extraction from car images followed by the segmentation of characters and reorganization and also develop electronics parking fee collection system based on number plate information.

Vehicle number plate recognition using mathematical morphology and neural networks

This paper presents a method for recognition of the vehicle number plate from the image using neural nets and mathematical morphology. The main theme is to use different morphological operations in such a way so that the number plate of the vehicle can be extracted efficiently. The method makes the extraction of the plate independent of color, size and location of number plate. The proposed approach can be divided into simple processes, which are, image enhancement, morphing transformation, morphological gradient, combination of resultant images and extracting the number plate from the objects that are left in the image. Then segmentation is applied to recognize the plate using neural network. This algorithm can quickly and correctly recognize the number plate from the vehicle image.

Real-Time Number Plate Reading

Vehicle number plate recognition systems are expected to have numerous applications in traffic surveying and monitoring, e.g. finding stolen cars and controlling access to car parks. In this paper algorithmic improvements to a previous version of a number plate reading system are described. The work builds on the experience gained by the Computer Vision Group at the University of Bristol. This paper addresses the problem of locating the number plate area in the image and proposes a new line based method for number plate location, which is suitable for realtime number plate recognition.

An associative processing module for a heterogeneous vision architecture

The heterogeneous vision architecture that satisfies the computing demands of real-time computer vision by providing parallelism in three different forms is described. A pipeline of digital signal processing (DSP) chips initially processes signals. Then a SIMD associative processor array processes images and extract features, and a MIMD network of transputers processes extracted objects in parallel. The array's VLSI implementation, the processing modes available due to the use of content-addressable memory, and the means of achieving efficient 2-D interprocessor communication in the linear array are described. An application as a vehicle number plate recognition system is presented. >