Monday, June 3, 2019

Automatic Number Pate Recognition System Information Technology Essay

Automatic Number Pate Recognition corpse Information Technology EssayAutomatic turning pate recognition system is a mass surveillance method that uses optical constituent recognition on epitomes to read the permit graduated t fittingfuls on vehicles. System might scan bout headquarterss at around star per second on cars traveling up to 100mph(160 km/h).they can use existing unappealing -circuit tele tidy sum or road-rule enforcement cameras, or ones specifically knowing for the task. They be employ by several(a) police forces and as a method of electronic doorbell collection on pay-per-use roads and monitoring business activity, such as red light ad presentnce in an intersection.ANPR can be used to gillyflower the images capture by the cameras as well as the text from the license plate, with nearly configurable to store a photograph of the driver. Systems commonly use infr argond discharge to allow the camera to take the picture at any time of the day. A powerf ul flash is inclined in at least one version of the intersection-monitoring cameras, serving both to illuminate the picture and to make the offender aware of his or her mistake. ANPR engineering science tends to be region -specific, owing to pate magnetic declination from place to place.Some concerns about these systems have centered on privacy fears of government tracking citizens movements and media reports of misidentification and high error rates. However, as they have authentic, the systems have operate much more accurate and reliable. There is an increasing requirement to identify vehicles and track their location for a wide number of applications. These includeCongestion charging several(prenominal) major cities around the world levy a charge a drive within themCar park management Using the number plate to identify the time of entry and departure of aVehicle.Counter-terrorism Monitoring the arrival and departures of vehicles at major ports.Literature ReviewOur literatu re survey mainly center on automatic number plate system research papers and its existing system along with its application, image bear upon technique and uneasy intercommunicate recognition. These can be clearly illustrated as followsAutomatic number plate recognition systemJavaanpr existing open source code in sourceforge.net thesis describing research, image processing and neural meshworking technique along with its algorithm in pdf on deep brownanpr on sourceforge.net mountain chain processing techniqueImageJ -api base on java language for digital image processingImage editor -api based on java language made for image processingJAI api -java advance imagination for image processing from sunmicrosystem atjava.sun.com.Opencv-Digital Image Processing (text book from library)Neural ne twainrking techniqueIntroduction to java neural network second fluctuation by jfheaton at heatonresearch.comSome ocr samples using neuralnetworking at sourcecode.com and its explanationStudy on nepali ocr research conducted by madan puraskar guthi(yala Maya Kendra)Ocr sample developed by Google based for Linux available for windows on dot net (tesseract)Joone engine-java api on neural network not so well developed and efficient athttp//www.joo freshlyorld.comKohenen -java api on egotism organizing map applied to compress jpeg image.Somdemo-sample java program for illustration how ego organizing map works.Program iterately train to converge with identical color from random samples painted neural network text book available at library (low price edition from pearsoneducation.Neural networks systematic introduction by Raul Rojas(from lectures at bring out university at Berlin and later at the university of Halle)Automatic Number Pate Recognition systema)javaanprJavaanpr open source available at sourceforge.net worked as prototype for kind our Nepali automatic Nepali number plate recognition system. It besides contain thesis in pdf format prescribing image processing technique and neural networking technique along with its algorithm. It works well recognizing foreign number plates contained as sample in the site. It was beautifully coded applying sophisticated and specialized algorithms for image processing and neural network technique. It also used xml saddles to save and retrieve neural network training data. figure of speech sample javaanpr at sourceforge.netFor more information-http//sourceforge.netImage processing proficiencya)ImageJ 1.42ImageJ was first developed on class files now available on GUI interface. User can just process image using various buttons and entries if prescription is required .programmers can develop own macros and plugins to achieve its intended function if required and compile there within and run the code.It is capable of processing both 2D and 3D interactive image processing.Figure. ImageJ graphical window interfaceFor more information http//rsb.info.nih.gov/ij/b) Image editorImage editor was also found during s earch for image processing tool. It is also based on java language and available as java API, now class file are available with GUI interface easing its its manipulation. Image editor api seems inefficient and not so capable for our intended operation and not so much researched.C) JAI apithe java advance imaging(JAI) API further extends the java platforms (including the java 2D API) by allowing sophisticated, high - action image processing to be incorporated into java applets and applications.JAI is a rophy of classes providing imaging functionality beyond that of Java 2D and the Java Foundation classes, though it is compatible with those APIs.JAI implements a set of core image processing capabilities including image tiling, regions of interest, and deferred execution.JAI also offers a set of core image processing operators including many common point, area, and frequency-domain operators.JAI is intended to meet the needs of all imaging applications. The API is highly extensible, a llowing new image processing operations to be added in such a way as to appear to be a native part of it. Thus, JAI benefits about all Java developers who want to incorporate imaging into their applets and applications.JAI distinctionsCross-platform imagingDistributed ImagingObject-oriented APIFlexible and ExtensibleDevice IndependentPowerfulHigh PerformanceinteroperableInitially program coding was done in JAI Later it becomes little inefficient and we again go for another programming method.For further information-http//java.sun.comd) Digital Image processing (text book from library)e) OpencvThe OpenCV implements a wide variety of tools for image interpretation. It is compatible with Intel Image Processing library (IPL) that implements low-level operations on digital images. In spite of primitives such as binarization, filtering, image statistics, pyramids, OpenCV is mostly a high-level library implementing algorithms for calibration techniques (Camera Calibration), feature dete ction (Feature) and tracking (Optical Flow),shape synopsis(Geometry, Contour Processing ),motion analysis (Motion Templates, Estimators ), 3D reconstruction (View Morphing),object class and recognition (Histogram, Embedded Hidden Markov Models, Eigen Objects).The essential features of the library along with functionality and quality is performance. The algorithms are based on highly negotiable data structures (Dynamic Data Structures) coupled with IPL data structures more than a half of the functions have been assembler optimized taking advantage of Intel Architecture (PentiumMMX,Pentium Pro, PentiumIII, Pentium4).Why We Need OpenCV LibraryThe OpenCV Library is a way of establishing an open source vision community thatWill make better use of up-to-date opportunities to apply computer vision in theGrowing PC environment. The software package provides a set of image processing functions,As well as image and pattern analysis functions. The functions are optimized for IntelArchitec ture processors, and are particularly effective at taking advantage of MMXTechnology.The OpenCV Library has platform-independent interface and supplied with whole CSources. OpenCV is open.Relation between Opens and Other LibrariesOpenCV is designed to be used together with Intel Image Processing Library (IPL)And extends the latter functionality toward image and pattern analysis. Therefore,OpenCV shares the same image format (IplImage) with IPL.Also, OpenCV uses Intel Integrated Performance Primitives (IPP) on lower-level, ifIt can locate the IPP binaries on startup.IPP provides cross-platform interface to highly-optimized low-level functions thatPerform domain-specific operations, particularly, image processing and computerVision primitive operations. IPP exists on multiple platforms including IA32, IA64,And StrongARM.Source-openCV beginning manual.pdfCmgui-wx-2(.net wrapper class)This openCV tool can be easily integrated with .net platform like c, visual basic etc.Cmgui is an adv anced 3D visual image software package with modeling capabilities.Cmgui is a part of CMISS, a mathematical modeling environment initially developed by the University of Auckland Bioengineering Institute.CMISS stands for Continuum Mechanics, Image analysis. signalize processing and System Identification. There are three major CMISS software packages. Broadly speaking the main areas each piece of software deals with are as followsCM is used for computational modelingUnemap is used for signal acquisition and processingCmgui is used for model visualization and manipulationFor more information-wiki/ getting started with cmguiNeural Networking techniquea) Introduction to java neural network by jeff heatonThis book along with video lecture helped very much for us to run into neural networks and learn coding technique. It was published form Heaton research center and they have developed encog framework for neural network where programmer can build fast neural network prototype for fast t esting and checking since easy and flexible. After parameters have been determined for best operation such as number of hidden layers and number of neurons in each layer coding can be done since it code will be inflexible for such modification. track record contained different chapters on various types of neural networks and also its application. Only first seven chapters are allowed to read online and rests are not. It provides all its source code on site which also helps in learning and testing.Same book is also available in c language.For more information-http//heatonreasearch.com/b) On the beginning of project research we also got OCR sample using neural network at sourcecode.com with explanation. It was written at c, due to compiler problem I didnt stress here much.c) Nepali OCRFor us it was good news and opportunity to study research on Nepali OCR conducted by madan puraskar guthi. Different research papers were available on the site along with image processing portion code u sed to fragment Nepali character Image written on java. It deals with problem issues and complexity faced on Nepali character like devnagari font.For more information -http//d) OCR engine tessaract by GoogleThis was used by Nepali OCR for its processing and it supports many languages like Hindi, Nepali, Urdu, arabi etc. we didnt research here much.Figure segmented portion ofFigure Another segmented portion ofFor more information-make Google search for referd) joone enginejoone engine as a api in hope for easy and efficient coding we consider but it seems unworthy for project work. For beginner liking to test some xor operations and similar may find at least satisfactory otherwise unworthy.For more information-http//www.jooneworld.com/docs/engine.htmle)KohenenThis sample also seems beautiful in understanding self organizing map or kohenen network. Here it is used to compress jpeg image. It was programmed on seven packages.For more information-http //f) som demoThis sample tries to converge iteratively with similar colors from randomly unlogical pixel colors based on Euclidean distance method.Figure som before trainingFigure som after trainingFor more information-link available at reference http//www.ai-junkie.com/ann/som/g) Artificial neural Network text book (library)h) Neural network systematic introduction (by Raul Rojas)This book is good for understanding neural network systematically and based on lectures at free university at Berlin and later at the University of Halle.For more introduction-reference at http//www.wikipedia.com/selforganisingmapFigure sample kohenen neural network (3D kohenen feature map)Source http//rfhs8012.fh-regensburg.de/jfroehl/index.htmlAnpr system application around worldPolice enforcementGermanyOn 11 March 2008, the Federal Constitution Court of Germany ruled that the laws permitting the use of automated number plate recognition systems in Germany violated te right to privacy.HungarySeveral Hungarian Auxiliary Police units us e a system called intercellular substance Police in cooperation with the police. It consists of a portable computer equipped with a webcam that scans the stolen car database using automatic number plate recognition. The system is installed on the splasher of selected patrol vehicles (PDA based handled versions exists as well) and is mainly used to control the license plate of parking cars, as the Auxiliary Police doesnt have the authority to invest moving vehicles to stop. If a stolen is found, the formal police are informed.United KingdomThe UK has an extensive (ANPR) automatic number plate recognition CCTV network. Effectively, the police and security function track all car movements around the country and are able to track any car in close to real time. Vehicle movements are stored for 5 years in the National ANPR Data Centre to be analyzed for intelligence and to be used as evidence.USAIn the USA, ANPR systems are more commonly referred to as LPR (License Plate Reader or Lice nse Plate Recognition) technology or ALPR (Automatic License Plate Reader/Recognition) technology.One of the biggest challenges with ALPR technology in the US is the accuracy of the Optical Character Recognition (OCR)-the actual identification of the characters on the license plate.From time to time, states will make significant changes in their license plate protocol that will affect OCR accuracy. They may add a character or add a new license plate design. ALPR systems must adapt to these changes quickly in order to be effective.In addition to the real-time processing of the license plate numbers, some ALPR systems in the US collect data at the time of each license plate capture .Data such as date and time stamps and GPS coordinates can be reviewed in relation to investigations and can help lead to critical breaks such as placing a suspect at a scene, witness identification, pattern recognition or the tracking of suspect individuals.Average Speed camerasAnother use of ANPR in the U K, Italy and Dubai (UAE) is for festinate cameras which work by tracking vehicles travel time between two flash-frozen points ,and therefore calculate the average speed. These cameras are claimed to have an advantage over traditional speed cameras in maintaining steady legal speeds over elongated distances, rather than encouraging heavy braking on approach to specific camera locations and subsequent acceleration back to illegal speeds.UKThe longest stretch of average speed cameras in the UK is found on the A77 road in Scotland, with 30 miles (48 km) being monitored between Glasgow and Ayr.ItalyIn Italian highways has developed a monitoring system named school covering more than 1244 km (2007). Further extensions will add 900 km before the end of 2008.The Tutor system is also able to intercept cars fleck changing lanes.Traffic controlMany cities and district have developed traffic control systems to help the movement and flow of vehicles around the road network. This had locall y involved looking at historical data, estimates, observations and statistics such asCar park usagePedestrian crossing usageNumber of vehicles along a roadAreas of low and high congestionFrequency, location and cause of road wordsThe UK Company Traffic master has used ANPR since 1998 to estimate average traffic speeds on non-motorway roads without the results being skewed by local fluctuations caused by traffic lights and similar. The company now operates a network of over 4000 ANPR cameras ,but claims that only the quartet most central digits are identified , and no number plate data is retained.Electronic toll collectionOntarios 407 ETR highway uses a combination of ANPR and radio receiver transponders to toll vehicles entering and exiting the road. piano tuner antennas are located at each junction and detect the transponders, logging the unique identify of each vehicle in much the same way as the ANPR system does.There are numerous other electronic toll collection networks whic h use combination of Radio frequency identification and ANPR. These includeBridge pass for the Saint John Harbor Bridge in Saint John New BrunswickCity link Eastlink in Melbourne, AustraliaGateway Motorway and Logan Motorway, Brisbane , AustraliaFast Trak in California ,United statesHighway 6 in IsraelTunnels in Hong Kong etcCharge zones the capital of the United Kingdom congestion chargeThe London congestion charge is an example of a system that charges motorists entering a payment area. Transport for London (TFL uses ANPR systems and charges motorists a daily fee of 8 pay(a) before 10pm if they enter, leave or move around within the congestion charge zone.Stockholm congestion evaluateIn Stockholm, Sweden, ANPR is used for the congestion tax of cars driving into or out of the inner city must pay a charge, depending on the time of the day.Other usesANPR systems may also be used for/bySection control, to measure average vehicle speed over longer distances.Border crossingsFilling s stations to log when a motorist drives away without paying for their fuel.A marketing tool to log patterns of useTraffic management systems, which determine traffic flow using the time it takes vehicles to pass two ANPR sites. force Through Customer Recognition, to automatically recognize customers based on their license plate and offer them their last selection, improving service to the customerTo assist visitant management systems in recognizing guest vehicles.Circumvention Techniques (drawback)Vehicles owners have used a variety of techniques in an attempt to evade ANPR systems and road -rule enforcement cameras in general. These methods may beincrease reflective properties of the lettering and so that system might no locate or produce high enough level of contrast to be able to readUse of plate cover or sprayUse of dirt to smear their license plate or utilize covers to mask the plateANPR imaging hardwareThe frontend of any Imaging hardware is image capturing device that is cam era. Retroreflective camera returns the light back to the source and thus improves the contrast of the image. A camera that makes use of active infrared light imaging (with a normal color filter over the lens and infrared illuminator next to it) benefits greatly from this as the infrared waves are reflected back from the plate. This is only possible on dedicated ANPR cameras, however, and so cameras used for other purposes must rely more heavily on the software capabilities.Figure hardware components used in ANPR systemFigure source-http//securityautomation.co.ukTo avoid blurring it is ideal to have the shutter speed of a dedicated camera set to 1/1000th of a second. License plate capture cameras can now produce usable images from vehicles traveling at 120 mph (190 km/h).threshold angles of incidence between camera lens and license plate are also major consideration to avoid image distortion during installation. Manufacturers have developed tools to eliminate errors from the physic al installation of license plate capture cameras.Research on down sampling characterFor neural network input character image is down sampled into matrix whose value is binary 1 or 0 according to Boolean property of character on matrix region.It showed that no of samples required is not fixed and it varies with thickness of font traced.Figure down sampling image character o with 7*5 matrixFigure downsampling same character image o (buffered) with 32 *35 matrixResearch works on algorithmsA new algorithm for character segmentation of license plateCharacter segmentation is an important step in License Plate Recognition (LPR) system. There are many difficulties in this step, such as the influence of image noise, plate frame, rivet, the space mark, and so on. This new algorithm presents character segmentation using Hough transformation and the prior knowl leaping in horizontal and upended segmentation respectively. Furthermore, a new object enhancement technique is used for image preproc essing. The experimentation results show a good performance of this new segmentation algorithm.Algorithm (steps)PreprocessingSize normalizationDetermination of plate kindObject enhancementHorizontal segmentation using Hough transformation erect segmentationFor more information-a new algorithm for character segmentation of license plate.pdfan adaptive thresholding algorithm for the augmented reality toolkitIt is well known that fixed global thresholds have adverse effects on the reliability of mug-based optical trackers under non-uniform lighting conditions. Mobile augmented reality applications, by their very nature, invite a certain level of robustness against varying external illumination from visual tracking algorithms currently AAR Toolkit depends on fixed-threshold image-binarization in order to detect prognosis fiducials for further processing. In an effort to minimize tracking failure due to uniform shadows and reflections on a marker surface, a fast algorithm for selectin g adaptive threshold values, based on the arithmetic mean of pixel intensities over a region-of- interest around candidate fiducials.AlgorithmThis works on a per-marker basis and evaluates the mean pixel luminance over a thresholding region-of -interest (ROI), which is defined as bounding rectangle around the markers axis -aligned corner vertices in screen space. If a marker has been detected in any addicted frame, its bounding rectangle will be used as thresholding -ROI prediction for successive frames. This method yields good thresholding level in practice, given sufficiently high video frame rates.Fig.1.reflection off a markers surface with adaptive thresholding (upper) and a global threshold (lower)For more information-10.1.1.9.4636.pdfadaptive license plate image extractionThis paper represents the automatic plate localization component of a car license plate recognition system. The approach concerns stages of preprocessing, edge detection, filtering, detection of the plates p osition, slope evaluation, and character segmentation and recognition. Single gray-level images are used as the only source of information. In the experiments Israeli and Bulgarian license plates were used, camera obtained at different daytime and whether conditions.Algorithm (step)preprocessing for plate candidate identificationvertical edge detectionrank filteringplate candidate segmentationvertical projection acquisitionprime clipping of the plateplate skew evaluationhorizontal segmentationplate candidate verificationCray-level distribution trunk considerations

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