Segmentation of characters on car license plates

License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE

Automatic vehicle identfication for Argentinean license plates using intelligent template matching

The problem of automatic number plate recognition (ANPR) has been studied from different aspects since the early 90s. Efficient approaches have been recently developed, particularly based on the features of the license plate representation used in different countries. This paper focuses on a novel approach to solving the ANPR problem for Argentinean license plates, called Intelligent Template Matching (ITM). We compare the performance obtained with other competitive approaches to robust pattern recognition (such as artificial neural networks), showing the advantages both in classification accuracy and training time. The approach can also be easily extended to other license plate representation systems different from the one used in Argentina.Fil: Gazcón, Nicolás Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Castro, Silvia Mabel. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Visualización yComputación Gráfica; Argentin

A Real-time Mobile Vehicle License Plate Detection and Recognition for vehicle monitoring and management

[[abstract]]In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. Relying on the instant vehicle body recognition, the system can detect and locate the vehicle license plate without the need of background image. Vehicle body detection system utilizes the color characteristics of the barking lights to carry out detection. It first detects the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights, thus quickly locate the license plate. This method can eliminate any environmental interference during the license plate detection. From the results of experiment, it is determined that this system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is dark, raining or under complicated environments.[[sponsorship]]IEEE Taipei Section; National Science Council; Ministry of Education; Tamkang University; Asia University; Providence University; The University of Aizu; Lanzhou University[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20091203~20091205[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Tamsui, Taipei, Taiwa

A Real-Time Mobile Vehicle License Plate Detection and Recognition

100學年度研究獎補助論文[[abstract]]In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. We utilize the color characteristics of the barking lights to carry out license plate detection. We first detect the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights. This method can eliminate any environmental interference during the license plate detection and improve the rate of accuracy of license plate detection and recognition. Moreover, we use the morphology method Black Top-Hat to enhance the level of separation of the license plate characters. Experiments show that the system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is in daytime, nighttime, clear day, raining day or under complicated environment.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Improving Parking Management in Downtown Nantucket

Nantucket is a small island with a historic charm that attracts numerous tourists during the summer months. This influx of tourists combined with the historic, narrow, cobblestone roads and a limited parking supply leads to a multitude of parking problems. The goals of our project were to improve traffic flow in the downtown area, and to improve parking management. To achieve these goals our team had to determine the current on-street conditions, proposes solutions, evaluate new parking management systems, and then solicit feedback from various stakeholders. Some of the results of this project are a list of potential street redesigns with photoshopped concept pictures, an improved parking inventory process, and a decision matrix for evaluating parking management technology systems

An End-to-End License Plate Localization and Recognition System

An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems

Study of object detection and reading(license plate detection and reading)

Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized. Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques. In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique. Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.</jats:p

Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system

The project “Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system” deals with detection and recognition of license plate from a captured front view of any car. The work follows all the steps in an ALPR system like preprocessing, segmentation, and license plate identification, extraction of individual characters and finally recognition of each character to form a string to match with the registered License plate numbers. The main contribution in the work is to expedite the number plate isolation from a set of segmented candidates. It utilizes a set of heuristics typically transition from object to background and vice-versa, aspect ratio of the bounding boxes. This narrow down the number of candidates for further processing and further, we suggest a rank based identification of each character in the number plate. The process scheme along with the existing methodologies is integrated to develop the overall ALPR system. A set of standard images collected from internet as well as self-collected car images of staff vehicles are used for simulation. The experiments are conducted using OpenCV. For validation, a working ALPR hardware prototype is developed using AVR development board (ATmega32 microcontroller), GP2D120 distance measurement sensor (IR-sensor).Interfacing between PC and controller-board is done using serial port. The model works with an accuracy of 80%. The ALPR system has a further scope to improve the recognition speed using parallel processing of various sub-steps