Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition (ANPR) systems have become one of the most important components in the current Intelligent Transportation Systems (ITS). In this paper, a FPGA implementation of a complete ANPR system which consists of Number Plate Localisation (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR) is presented. The Mentor Graphics RC240 FPGA development board was used for the implementation, where only 80% of the available on-chip slices of a Virtex-4 LX60 FPGA have been used. The whole system runs with a maximum frequency of 57.6 MHz and is capable of processing one image in 11ms with a successful recognition rate of 93%

Towards Accessible Self-service Kiosks through Intelligent User Interfaces

Public self-service kiosks provide key services such as ticket sales, airport check-in and general information. Such kiosks must be universally designed to be used by society at large, irrespective of the individual users’ physical and cognitive abilities, level of education and familiarity with the system. The noble goal of universal accessibility is hard to achieve. This study reports experiences with a universally designed kiosk prototype based on a multimodal intelligent user interface that adapts to the user’s physical characteristics. The user interacts with the system via a tall rectangular touch-sensitive display where the interaction area is adjusted to fit the user’s height. A digital camera is used to measure the user’s approximate reading distance from the display such that the text size can be adjusted accordingly. The user’s touch target accuracy is measured, and the target sizes are increased for users with motor difficulties. A Byzantine visualization technique is employed to exploit unused and unreachable screen real estate to provide the user with additional visual cues. The techniques explored in this study have potential for most public self-service kiosks

Fuzzy and Neural Network Based License- Plate Localization and Recognition

ABSTRACT: This paper presents the extraction of vehicle license plate information from a sequence of various images. ALPR is used in the presence or absence of a database in various applications such as, toll payment, etc. The proposed technique implements the CAN image by using a camera and to extract the license plate from the image based on various characteristics, such as the boundary, the color, or the existence of the characters. Thus it is not suitable for location of variable number plate. Finally to recognize the extracted characters by template matching by using neural networks and fuzzy classifiers. KEYWORDS: vertical edge detection algorithm (VEDA), scale-invariant feature transform(SIFT), dynamic programming(DP). Observing the notion of cars and do not belong in a parking garage. I.INTRODUCTION Automated By eliminating the parking as expedited which is the need for human confirmation of parking passes. The organization of the paper is mentioned as follows. In section II, a detailed review of ALPR Technique. Section III , illustrates the various algorithms used in ALPR Technique. Section IV features out the proposed method. Section V and VI narrates the experimental and simulation results and Section VII concludes the paper and defines the future works. ISSN (Print Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8144 II. ALPR TECHNIQUE ALPR is known by several other names, including Automatic Number Plate Recognition (ANPR), Automatic Vehicle Identification (AVI), Car Plate Recognition (CPR), License Plate Recognition (LPR), and Lecture Automatique de Plaquesd' Immatriculation (LAPI). Other name of ALPR are followed as car plate recognition, automatic vehicle identification, and optical character recognition for cars Optical character recognition, usually abbreviated as OCR, is conversion by electronic and mechanical of scanned images which may be of writing by hand, or printed characters and numbers into machine-encoded format of characters and numbers. It is widely used as a form of data entry from some sort of original paper data source, whether documents, sales receipts, mail, or any number of printed records It is a common method of digitizing printed characters and numbers so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, textto-speech and text mining[6]- Early versions needed to be programmed with images of each and every texts at a time implied on a single font. "Intelligent" systems with a greater degree of recognition accuracy for most fonts are now common III. ALGORITHMS USED IN ALPR TECHNIQUE The ALPR system comprising of four levels from the input image results during the extraction of a license plate number. The first level is the location of an image of a car by utilizing a camera. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8145 The second level is the extraction of a license plate from the given input image based on the following features as boundary, existence of the characters and the color. The third level is the character extraction and segmentation of license plate. The final level is the recognization of character extraction by the matching of templates such as fuzzy classifiers. This paper illustrates the procedural types of various algorithms, They are as follows: A. Block converter Block converter is the converter ,which is used to convert the image to sub blocks from the sub block , use selected box only for segmenting the number plate. The block-based method is also presented in which the blocks with greater magnitude edges are visualized as areas of license plate. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8146 B. Otsu The number plate consist of fore ground information and back ground data. In this algorithm the necessity of numbers only utilize the threshold segmentation. C. State-of-Art It is the feature based process. Training data convert to feature points. Feature point only depends upon the shape of the training image. Then it is compared to the number plate feature points Backgrounds of license plate and characters has varying colors, possessing opposite binary values in the binary image. Horizontal projection of used to extract the characters along with noise removal and analyzing the simplicity. To convert grayscale im-age into a binary image by using a threshold operation. There are basically two types of threshold operation

Indian License Plate Recognition – A Review

Automatic Number Plate Recognition (ANPR) is a real time embedded system which identifies the characters directly from the image of the license plate. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system are different for each country. License plate detection is an important stage in vehicle license plate recognition for intelligent transport systems. This paper presents approaches for license plate detection for Indian license plates. The basic step of license plate detection is localization of number plate. An extensive experimentation has been carried out for various types of Indian license plate to verify and authenticate the results. The aim of this paper is to study and evaluates accuracy at each stage of license plate detection algorithms

DISEÑO DE UN ALGORITMO DE RECONOCIMIENTO DE PLACAS VEHICULARES ECUATORIANAS USANDO REDES NEURONALES CONVOLUCIONALES

Detecting stolen vehicles in Ecuador is one of the important tasks for the country’s security agencies. One of the ways to increase the detection and recovery rate of vehicles is through intelligent applications, based on artificial intelligence and neural networks. These applications allowed the development of new vehicle license plate recognition (LPR) techniques. In this article, we propose a model to detect moving car plates using convolutional neural networks. First, a tagged manual is made on the images of the vehicle, identifying the location of the plate and the characters within the image. This information is presented in a convolutional neural network architecture for training and testing. The network architecture developed by Google, COCO Inception V2, is used as a training base, where we get our own trained model for Ecuadorian vehicles plates. The experimental results find that the present work achieves a favorable recognition precision of 85.1 in terms of the Ecuadorian plate training photo data set. It is worth specifying the exclusive use of open source software for the development of this work.La detección de vehículos robados en Ecuador es una de las tareas más importantes para las agencias de seguridad del país. Una de las formas de aumentar la tasa de detección y recuperación de vehículos es a través de aplicaciones inteligentes, basadas en inteligencia artificial y redes neuronales. Estas aplicaciones permiten el desarrollo de nuevas técnicas de reconocimiento de placas vehiculares (LPR). En este artículo, proponemos un modelo para detectar placas de automóviles en movimiento utilizando redes neuronales convolucionales, primero se realiza un etiquetado manual sobre las imágenes de los vehículos, identificando la ubicación de la placa y caracteres dentro de la imagen. Esta información se introduce en una arquitectura de redes neuronales convolucionales para entrenamiento y pruebas. La arquitectura de la red neuronal es desarrollada por Google, COCO Inception V2, y se utiliza como base de entrenamiento, de donde se obtiene un modelo propio entrenado para placas ecuatorianas. Los resultados experimentales muestran que el presente trabajo alcanza una precisión de reconocimiento favorable de 85.1 % en términos del conjunto de datos de fotos de entrenamiento de placas ecuatorianas. Vale la pena mencionar el uso exclusivo de software de código abierto para el desarrollo

Real Time Automatic Number Plate Recognition Using Morphological Algorithm

The rising increase of up to date urban and national road networks over the last three decades become known the need of capable monitoring and management of road traffic. Expected techniques for traffic measurements, such as inductive loops, sensors or EM microwave detectors, endure from sober shortcomings, luxurious to install, they demand traffic distraction during installation or maintenance, they are massive and they are unable to notice slow or momentary stop vehicles. On the divergent, systems that are based on video are simple to install, use the existing infrastructure of traffic observation. Currently most reliable method is through the detection of number plates, i.e., automatic number plate recognition (ANPR), which is also branded as automatic license plate recognition (ALPR), or radio frequency transponders. The first revalent step of information is finding of moving objects in video streams and background subtraction is a very accepted approach for foreground segmentation. Next step is License plate extraction which is an essential stage in license plate recognition for automatic transport system. We are planned for two ways for removal of license plates and comparing it with other existing methods. The Extracted license plates are segmented into particular characters by means of a region-based manner. The recognition scheme unites adaptive iterative thresholding with a template matching algorithm. The method is strong to illumination, character size and thickness, skew and small character breaks. The main reward of this system is its real-time capability and that it does not require any extra sensor input (e.g. from infrared sensors) except a video stream. This system is judged on a huge number of vehicle images and videos. The system is also computationally extremely efficient and it is appropriate for others related image recognition applications. This system has broad choice of applications such as access control, ringing, border patrol, traffic control, finding stolen cars, etc. Furthermore, this technology does not need any fitting on cars, such as transmitter or responder

Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases

Real Time Vehicle License Plate Recognition on Mobile Devices

Automatic license plate recognition is useful in many contexts such as parking control, law enforcement and vehicle background checking. The high cost and low portability of commercial systems makes them inaccessible to the majority of end users. However, current mobile devices now have processors and cameras that make image processing and recognition applications feasible on them. This thesis investigates high accuracy real-time license plate recognition on a smartphone, taking into account device limitations. It first explores how, using the minimal image processing and simple configurable heuristics based on plate geometry, license plates and their characters can be detected in an image. Then, using minimal training data, it shows that a character recognition package can achieve high levels of accuracy. This approach accurately recognized 99 percent of plates appearing in a test set of videos of vehicles with New Zealand license plates

Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos

Automatic video analysis from traffic surveillance cameras is a fast-emerging field based on computer vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient management of traffic. In recent years, there has been an increased scope for automatic analysis of traffic activity. We define video analytics as computer-vision-based surveillance algorithms and systems to extract contextual information from video. In traffic scenarios several monitoring objectives can be supported by the application of computer vision and pattern recognition techniques, including the detection of traffic violations (e.g., illegal turns and one-way streets) and the identification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the recognition of number plates, i.e., automatic number plate recognition (ANPR), which is also known as automatic license plate recognition (ALPR), or radio frequency transponders. Here full-featured automatic system for vehicle detection, tracking and license plate recognition is presented. This system has many applications in pattern recognition and machine vision and they ranges from complex security systems to common areas and from parking admission to urban traffic control. This system has complex characteristics due to diverse effects as fog, rain, shadows, uneven illumination conditions, occlusion, variable distances, velocity of car, scene's angle in frame, rotation of plate, number of vehicles in the scene and others. The main objective of this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from video acquisition to optical character recognition are considered to achieve an automatic identification of plates