Дисертації: "NUMBER PLATE RECOGNITION SYSTEM"

1

Zhai, Xiaojun. "Automatic number plate recognition on FPGA." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/14231.

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Intelligent Transportation Systems (ITSs) play an important role in modern traffic management, which can be divided into intelligent infrastructure systems and intelligent vehicle systems. Automatic Number Plate Recognition systems (ANPRs) are one of infrastructure systems that allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. ANPR is a well proven technology that is widely used throughout the world by both public and commercial organisations. There are a wide variety of commercial uses for the technology that include automatic congestion charge systems, access control and tracing of stolen cars. The fundamental requirements of an ANPR system are image capture using an ANPR camera and processing of the captured image. The image processing part, which is a computationally intensive task, includes three stages: Number Plate Localisation (NPL), Character Segmentation (CS) and Optical Character Recognition (OCR). The common hardware choice for its implementation is often high performance workstations. However, the cost, compactness and power issues that come with these solutions motivate the search for other platforms. Recent improvements in low-power high-performance Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) for image processing have motivated researchers to consider them as a low cost solution for accelerating such computationally intensive tasks. Current ANPR systems generally use a separate camera and a stand-alone computer for processing. By optimising the ANPR algorithms to take specific advantages of technical features and innovations available within new FPGAs, such as low power consumption, development time, and vast on-chip resources, it will be possible to replace the high performance roadside computers with small in-camera dedicated platforms. In spite of this, costs associated with the computational resources required for complex algorithms together with limited memory have hindered the development of embedded vision platforms. The work described in this thesis is concerned with the development of a range of image processing algorithms for NPL, CS and OCR and corresponding FPGA architectures. MATLAB implementations have been used as a proof of concept for the proposed algorithms prior to the hardware implementation. The proposed architectures are speed/area efficient architectures, which have been implemented and verified using the Mentor Graphics RC240 FPGA development board equipped with a 4M Gates Xilinx Virtex-4 LX40. The proposed NPL architecture can localise a number plate in 4.7 ms whilst achieving a 97.8% localisation rate and consuming only 33% of the available area of the Virtex-4 FPGA. The proposed CS architecture can segment the characters within a NP image in 0.2-1.4 ms with 97.7% successful segmentation rate and consumes only 11% of the Virtex-4 FPGA on-chip resources. The proposed OCR architecture can recognise a character in 0.7 ms with 97.3% successful recognition rate and consumes only 23% of the Virtex-4 FPGA available area. In addition to the three main stages, two pre-processing stages which consist of image binarisation, rotation and resizing are also proposed to link these stages together. These stages consume 9% of the available FPGA on-chip resources. The overall results achieved show that the entire ANPR system can be implemented on a single FPGA that can be placed within an ANPR camera housing to create a stand-alone unit. As the benefits of this are drastically improve energy efficiency and removing the need for the installation and cabling costs associated with bulky PCs situated in expensive, cooled, waterproof roadside cabinets.

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2

Akinola, Paul. "Design and Implementation of an IoT Solution for Vehicle Access Control in Residential Environment." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97047.

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To overcome the hurdles associated with space management and security controls in a housing system, research was projected to study and analyze the necessary factors of accomplishment. Over time, different processes were observed and reviewed to make this a possible deal. Various residents were interviewed on the daily constraints in parking and managing their vehicles within their housing premises. The reported daunting concern was majorly the gate access and personal hunts for the space to keep the individual resident’s cars. Every resident would always have to stop and hoot at the housing gate for the assigned personnel to check and open the gate. While this would waste every resident’s time, the visitors even face more delay often time. Hitherto, car access and parking constraint become a thing of worry that no one would want to engage the housing service anymore. The interest has got dwindled. And to re-awaken the high patronage of the housing system, a gap must be bridged with an immediate solution to space management with a gating system. These were subsequently given a classical thought, while a prototype solution was demonstrated and reviewed with the various residents of some selected housing. This received a high welcoming embracement and was beckoned to be made real by the logical heuristic. At this point, nothing was further considered than using the Internet of things (IoT) technology to implement Vehicular Access Management for the control and integration of intended space provisioning in any housings. Consequently, the number plate of every vehicle becomes the automatic access tag and would be used for security control within the housing location. Vehicles’ numbers would be captured and used to manage the residents passing through the automated gating system. With it, records would be made for all permitted residents and the visitors that own a car. Thus, a proper arrangement would be allotted accordingly, as provisioned by the gating system administrator. However, to allegories the above-proffered solution, this project work is divided into six sections. The introductory section introduces the project rationale, lists the objectives, explores related works, and introduces how IoT and vehicular systems can be merged. The second section delves into these vehicular systems. It introduces the Automatic License Plate Recognition System (ALRP) and the Raspberry Pi and highlights the merits of the Integrated Vehicular Access Security System. Open-CV and machine learning are also introduced. Section three covers the solution design, while section four is the implementation phase. Section five covers the testing and implementation of the solution. The final section summarizes the project. The project successfully models an automated solution for the security of tenants and vehicle users against unauthorized access to residential estates and buildings.

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3

Robinson, Alan. "Validating traffic models using large-scale automatic number plate recognition (ANPR) data." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/66238.

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Traditional manual survey methods for collecting reliable origin-destination data to develop large strategic transport model is notoriously expensive and the sample sizes are often relatively small. Arguably, the least reliable data required for the development of strategic traffic models is the origin-destination data. Recent technological advances, such as probe data from on-board devices, have been successful in providing data for some needs such as journey times and routing options. However, varying degrees of success have been achieved in obtaining reliable origin-destination (OD) data from these new technologies. Automatic Number Plate Recognition (ANPR) is one if the newer technologies that could be used to collect large-scale data sets over the large study areas that strategic traffic models cover. The aim of this study is to examine ANPR data collected from the Gauteng Freeway Improvement Project's (GFIP) Open Road Tolling (ORT) gantries in terms of its accuracy and uses in the development and improvement of strategic traffic models. Of particular interest is the use of the ANPR data to contribute towards the improvement of the distribution of trips in the OD matrices. This is achieved by developing methodologies to derive comparable gantry to gantry traffic volumes from the ANPR data and the GFIP traffic model. The above comparisons enabled the undertaking of a post opening project evaluation of the GFIP traffic model's 2015 forecasts using as many characteristics of the traffic flows and patterns that can be derived from the ANPR data. Characteristics such as traffic volumes and journey times are directly comparable with standard traffic model outputs. Tracking vehicles between gantries enabled the calculation of the number of trips that travel between gantry pairs giving rise to gantry-to-gantry (G2G) trips, which can be represented in a G2G count matrix. This G2G count matrix has probably the most beneficial data that can be derived from the ANPR systems as it contains an "accurate" element of the trip distribution on the road network. A methodology was developed to derive equivalent trip matrices from a traffic model's select-link trip matrices where the links are those where the gantry (ANPR camera) is located. The sums of the trips in the derived sub-matrices match the G2G counts. This enabled the comparison between the modelled trip distribution represented by the select link to select link (SL2SL) volumes and the actual ANPR G2G counts. This is in fact a comparison of a portion of the model's distribution to actual, comprehensive data. This study demonstrates that ANPR data has the potential to improve strategic traffic models. The automation of the processes to derive the SL2SL assigned volumes from the models and combining it with existing matrix estimation techniques will enhance the trip distribution in the output trip matrix. The current practice of using individual traffic counts in matrix estimation has the adverse tendency to affect the trip distribution. Hence, the recommendation to use traffic counts in matrix estimation to traffic counts with caution.
Dissertation (MEng)--University of Pretoria, 2017.
Civil Engineering
MEng
Unrestricted

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4

Haines, Alina. "The role of automatic number plate recognition surveillance within policing and public reassurance." Thesis, University of Huddersfield, 2009. http://eprints.hud.ac.uk/id/eprint/8760/.

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This Thesis examines the role that Automatic Number Plate Recognition surveillance plays within policing and public reassurance. The thesis is improvement orientated, exploring how ANPR could become a more effective policing tool and highlights implications for future policies and practice. The first two chapters set the context for the research, explaining what ANPR is, its place within criminology and gaps in research addressed in the Thesis. The literature review calls for a better understanding of ANPR’s potential and role as an investigative tool and an examination of the public’s views about ANPR surveillance. In the third chapter, reference is made to available methods used to address such objectives. Chapters Four, Five and Six present the results emerging from the empirical work in this Thesis. Chapter Four is concerned with police perceptions regarding current ANPR practice. The thesis highlights the complexity of translating policy into practice in the current political and economic climate, where objectives and priorities dictated by the government are constantly shifting. Continuing its improvement orientation, Chapter Five covers public perceptions about ANPR and outlines ways to address the balance between privacy and security without endangering both. The potential impact of ANPR on crime and ways to measure it is the topic of Chapter Six, which argues that establishing a causal link between ANPR and crime is not a straightforward process. The concluding chapter talks about the implications of the study and any interesting future avenues for research. The emerging findings from this research sit uncomfortably with the opinions and predictions of both supporters and opponents of ANPR alike and shed light not only on the management and use of ANPR by the police in Britain, but also on many of the ethical issues raised by the emergence of new surveillance technologies.

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5

Johnson, Abioseh Saeley. "Automatic number-plate recognition : an application of computer vision technology to automatic vehicle identification." Thesis, University of Bristol, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300053.

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6

Gunaydin, Ali Gokay. "A Constraint Based Real-time License Plate Recognition System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608195/index.pdf.

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License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate localization algorithm is based on vertical edge features where constraints are used to filter out non-text regions. Vertical and horizontal projections are used for character segmentation and Multi Layered Perceptron (MLP) based Optical Character Recognition (OCR) module has been implemented for character identification. The extracted license plate characters are validated against possible license plate formats during the recognition process. The system is tested both with Turkish and foreign license plate images including various plate orientation, image quality and size. An accuracy of 92% is achieved for license plate localization and %88 for character segmentation and recognition.

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7

Setchell, Christopher John. "Applications of computer vision to road-traffic monitoring." Thesis, University of Bristol, 1998. http://hdl.handle.net/1983/a79e87e2-8020-45ce-be27-dd9e382d18c7.

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8

Liaqat, Ahmad Gull. "Mobile Real-Time License Plate Recognition." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-15944.

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License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation and Optical Character Recognition (OCR). For License Plate Detection and character segmentation, we used JavaCV and OpenCV libraries. And for OCR, we used tesseract-ocr. We obtained very good results by using these libraries. We also stored records of license numbers in database and for that purpose SQLite has been used.

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9

JAIN, URVASHI B. "AUTOMATED NUMBER PLATE RECOGNITION SYSTEM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14455.

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ABSTRACT The strategy of the project is to design automatic system for recognizing the number plate of the car from image taken by the traffic surveillance system. Thus, the project is to investigate and construct an application whereby the system will recognize the car number plate from the image captured of the car. The system will be based on a personal computer and software packages available, MATLAB program and a digital camera, which help to capture the images of the car. The general algorithm involves the following steps: Ø Image Processing: The image captured is preprocessed and reduction in the contrast. Ø Conversion of RGB image to Gray image: Processed image is converted into a gray image. Ø Erosion and Dilation: Unwanted noise is removed. Ø Localization of number plate using Histogram approach: Thresholding is applied to both the row and column difference vectors obtained. Ø Smoothening and Filtering: Unwanted rows and columns are removed using averaging method. Ø Final extraction of number plate: Final number plate is extracted by selecting the area having maximum intensity variation between pixels and having maximum row and column sum of pixel intensity. Ø Number plate recognition based on OCR method.

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10

Fang, Jiung-Bin, and 方俊斌. "A Study of Car-Plate Number Recognition System." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/74488032540833718528.

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碩士
國立成功大學
工程科學系
87
Number plate recognition system requires a series of complex image processing steps. In this thesis, a number plate recognition system had been developed. The system includes three phases called pre-processing , ROI(Region of Interest) selection, and character segmentation & recognition. In the first phase, the gray value contrast of the input image is adjusted properly. In the second phase, the area probably contains the number plate is selected by using line based scanning method. In order to avoid the false selection causing by decoration, one to three Region of Interest areas are held. And the scanning is made from left to right, bottom to top. To decide the ROI area, the property of interleaving of black and white pattern for a number plate is used. In the third phase, the characters of the number plate are firstly segmented out by using the line scanning method. The scanning is made from top to bottom of the ROI and accumulated the number of black pixels for the scanned line. The accumulated number is used to evaluate if the line belongs the member of a character. After the segmentation, the character image is binarized and normalized with size 20*10 pixels. Finally, back propagation neural network is used to recognize the segmented characters. 187 data samples are randomly separately into 93 samples for training and 94 samples for testing. Then extra 101 images are used to evaluate the system. The successful rate is about 98%, and the average processing time is 0.7sec for each plate’s recognition.

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11

Chuang, Ming-Hui, and 莊明輝. "Applying Tesseract-OCR to a Number Plate Recognition System Development." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/18620796778916972257.

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碩士
國立臺灣海洋大學
電機工程學系
103
Science and technology is more and more flourishing , that the vehicle is to be need in each family. The vehicle is already one part of life, and amount of world vehicle also at keep on on the increase, the public order transportation caused by a great deal of car is as the most important as management problem. The car has already become main vehicle at present, and then the license plate is like the identity card of car, want the words to carry on the effective management to car are from the license plate begin most physically. It is already a necessary tool to recognize for the sake of solution above problem license plate. This thesis proposes the image processing and Tesseract-OCR technology in the implementation of a number plate recognition system . The related image processing need for license plate recognition technology is being realized by the open source computer vision library (OpenCV). Image processing using color conversion to convert the color image to a grayscale image and using fuzzy processed technique for reduce to the noise. License plate positioning are processed using edge detection and morphology method , which look for some images like the license plate and to sieve it out and we using some characteristics to choose the correct license plate.After using image processing to license plate image and to eliminate that not belong to the license plate character image.Then the license plate character image had be find out to use Tesseract-OCR to identify that the find character image to compare together and to obtain similarity a number plate character. In this thesis the Tesseract-OCR is an open source of optical character recognition engine, it can support many character and language to identify and with open source computer vision library(OpenCV),on the Internet is easy tool to be obtained . The proposed system can be quickly implemented in the multiple platforms and shorten development time . The system can be useful in many areas,because it can change according to different countries to identify characters .

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12

Jia, W. "Number Plate Detection (NPD) algorithm." Thesis, 2006. http://hdl.handle.net/10453/37716.

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University of Technology, Sydney. Faculty of Information Technology.
Automatic Number Plate Recognition (ANPR) is an important Intelligent Transportation System (ITS) technology, which distinguishes each vehicle as unique by recognising the characters in their number plates via image analysis and pattern recognition techniques. In an ANPR system, the most crucial part is number plate detection. The research presented in this thesis focuses on the detection mechanism and will rely on a third-party Optical Character Recognition (OCR) software for character recognition. Number Plate Detection (NPD) is a well-explored problem with many successful solutions. Although most of these solutions are reasonably fast and robust, they can be further improved to make them even faster and more robust to deal with various complex conditions in real-time. This thesis first presents a region-based NPD algorithm, which provides much more accurate detection results than previous NPD algorithms and is robust against interference characters in images. Then, a fast and robust edge-based NPD algorithm is developed. Tins algorithm can detect various number plates under various conditions in real-time with a high detection rate and a very low false positive rate. Similar work has not been reported elsewhere. Besides character information, the colour information of number plates also plays an important role in identifying each number plate as unique. Hence, this thesis also develops algorithms for classifying number plate colours. Histogram-based image matching methods are investigated, and a Gaussian Weighted Histogram Intersection (GWHI) algorithm is presented. This algorithm is shown to be much more robust against various colour variations than previous methods. Furthermore, a novel Colour Edge Co-occurrence Histogram (CECH) method is presented. It is shown to be particularly applicable for rapidly matching compound objects, such as number plates. Finally, histogram-based image matching technique on a hexagonal image structure is investigated. Gevers' idea of using Colour Ratio Gradient (CRG) for robust object matching is redefined on hexagonal structure, arid a novel Symmetric Colour Ratio Gradient (SCRG) method is developed. Experimental results demonstrate that the proposed SCRG method outperforms the Gevers’ CRG method. More contributions can be found in the appendices. A new virtual hexagonal structure is proposed, on which the time used for mapping a square-based image to hexagon-based image is dramatically reduced. Two basic image transformation operations and a novel edge detection algorithm are performed on the new structure. The results obtained in this thesis can also be applied to many other areas such as Character Detection, Text Detection, and Image/Video Retrieval

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13

Huang, Min-Siang, and 黃敏翔. "Development of a Number Plate Recognition System Using Artificial Neural Networks and Support Vector Machines." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/r3bf5q.

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碩士
國立臺灣海洋大學
電機工程學系
103
This thesis proposes the application of artificial neural networks (ANN) and support vector machines (SVM) in the implementation of license plate recognition systems. In this work, the proposed license plate recognition system can be divided into three stages, namely image pretreatment, vehicle license plate location and character recognition. The related image processing required for license plate recognition technology is being realized by the open source computer vision library (OpenCV). Image processing using color conversion will convert the color image to a grayscale image and using fuzzy processing technique for the treatment of the noise. License plate positioning is processed using edge detection method, which look for images in the blocks of interest and put into the stay classification category. The images of interest are then processed using SVM for correct identification of the license plate image. Character identification is performed using Otsu threshold technology which can discriminate the character from the background image. Based on the concept of morphology, the unwanted noises are filtered out. The outline screening technology is then incorporated to extract the image font characteristic values. Finally, the completion of the car license number character identification can be accomplished using the artificial nerve network multilayer perceptron. In this work, the proposed license plate recognition system is being realized using C ++ with open source computer vision library (OpenCV).The proposed scheme can be quickly implemented in the embedded vision system and shorten development time. The system can be useful in many areas such as smart car parking system, identification of stolen vehicle etc,.

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14

Larsson, Stefan, and Filip Mellqvist. "Automatic Number Plate Recognition for Android." Thesis, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72573.

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This thesis describes how we utilize machine learning and image preprocessing to create a system that can extract a license plate number by taking a picture of a car with an Android smartphone. This project was provided by ÅF at the behalf of one of their customers who wanted to make the workflow of their employees more efficient. The two main techniques of this project are object detection to detect license plates and optical character recognition to then read them. In between are several different image preprocessing techniques to make the images as readable as possible. These techniques mainly includes skewing and color distorting the image. The object detection consists of a convolutional neural network using the You Only Look Once technique, trained by us using Darkflow. When using our final product to read license plates of expected quality in our evaluation phase, we found that 94.8% of them were read correctly. Without our image preprocessing, this was reduced to only 7.95%.

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15

Chow, Jiunn Nan, and 周俊男. "License Plate Recognition System." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/35737856446114458856.

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碩士
國立中山大學
資訊工程研究所
83
In this thesis, we develop a license plate recognition system. The system first automatically locate the location of license plate ,and then use the mathematical morphology to do recognition. Traditional character recognition systems use segmentation, thinning, stroke detection and so on. From the strokes relationship, they use a similarity measure to select best matched character. In this thesis, we use the mathematical morphology method to extract the properties of each character by a sequence of morphology operations. Then we can determine the character by a decision tree which is built based on the properties of the numerals and alphabets. The license plate recognition system can be devided into three parts : preprocessing, license plate localization, and character recognition. In the first part, it includes image acquirement, thresholding, and noise removal. The second part is to find the location of the license plate by the connected components analysis. The last part is mainly a character recognition system. In experiment, we take 40 images as samples. The results show that the successful rate is 90%, the unrecognized rate is 7.5% and the misjudged rate is 2.5%.

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16

Lee, Chia-Wen, and 李嘉雯. "Vehicle Plate Recognition System." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/92627493463048928965.

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Анотація:

碩士
國立臺灣科技大學
電機工程系
88
A vehicle plate recognition system is expected to have numerous applications, such as building automation and security, electric payment toll-gate, parking lot management, and etc. In this paper, we have developed a fast and effective vehicle plate image recognition system which can automatically locate the position information of a vehicle plate from the source image. According to the position information, we can process the image locally, and then extract the characters on vehicle plate and finally recognize them. The vehicle plate recognition system can be divided into three phases: (1)pre-processing; (2)vehicle plate locating and characters extracting; (3) the vehicle plate characters recognition. In pre-processing, we process our source image with the compensation of interlaced phenomenon, gray scaling, bi-level quantization and noise cancellation. In the part of vehicle plate located and character extracting, we locate the position of a vehicle plate by using the “crossing” information of the vehicle plate characters in horizontal direction; furthermore we extract those characters image by the histogram of the local image. In the recognition part, we have built a weighting matrix from the matching patterns with their statistic property. We can count the weighting sum to recognize those characters to get the correct vehicle identification number. Our vehicle plate recognition system is experimented with 185 car images via a PC with 300 MHz CPU and 128 MB RAM. The recognition rate is 91.4 and the average processing time is 1.45 sec.

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17

Hsu, Po-Cheng, and 許伯誠. "License Plate Recognition System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/05825350829541238670.

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Анотація:

碩士
國立高雄第一科技大學
電腦與通訊工程所
95
License plate recognition system can be used extensively in management of parking lot and appliance of stolen car searched、road controlled，car controlled of police unit. However, one of the reasons of unable to popularize is that there is still a lot to improve in distinguish technology. For example, license plate orient rate、word distinguish rate、resist background noise ability, etc. Therefore, the thesis of this research paper is to improve the accuracy of auto license plate recognition system. Due to the full-developed image processing technology nowadays, the thesis is to develop an automatic license plate recognition system by using image processing technology. The license plate recognition system in the paper is mainly divided into three part, license plate location、word takeout and word distinguishing. This system using the image processing technology to location the license plate, and take out the word on the license plate. Word distinguishing is using the projection method. There are some restrictions in using this automatic license plate recognition system. For instance, if the shoot environment is outdoors, environmental background will be complicated. Trees, signboard, traffic symbol, etc, will cause the difficulty to location the license plate. Thus, the system has to overcome the problem of background complexity. We propose a license plate recognition system which can be adapted to background complexity and have a multiple distinguishing computing mechanism. Cooperate with the restriction of some threshold value, we can distinguish out the license plate number in the license plate finally. This system takes totally 271 photos in indoor and outdoor parking areas to experiment. The result shows that the average in license plate location is 96.31 %, in word takeout is 93.87 %, and in word distinguishing is 95.96 %, and the totally distinguish rate is 88.79 % . In the system is Pentium 4, CPU 3.4GHz, 1GB　RAM , each image nearly take to distinguish time is 3 Second.

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18

Zeng, Shi-Hao, and 曾士豪. "Automatic Car Plate Recognition System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/qafkj5.

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Анотація:

碩士
國立交通大學
電控工程研究所
105
Car plate recognition system is widely used in all aspects of life, such as parking lot management system and highway toll collection. However, existing recognition systems are limited by image processing. To recognize car plates instantly, extra sensors are installed on most of systems, or a region of interest is defined on single lane. 　Our aims of research are to process surveillance video directly without additional devices, and to achieve multi-lane recognition in real time. By principal component analysis, we can separate the cars from the background, therefore plenty of processing time has been saved. We also develop the robust recognition module based on different samples, which are influenced by environmental factors such as reflection, dirt and shadow. The module implements plate segmentation by Sobel edge detector and Gaussian filter, analyzes connected component in plate to segment characters and uses support vector machine to recognize characters. 　In the last chapter, we examine the system with a high-definition video. The result show that our system can recognize the simulation video in real time, and the overall rate of success is 91.35%.

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Tsai, Chien-Tsai, and 蔡建材. "Automatic License Plate Recognition system." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/48475416792944540060.

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碩士
中華大學
資訊工程學系碩士班
93
A real-time automatic license plate recognition system is proposed in this paper to identify the license plate quickly and accurately in the unrestricted environment, e.g. different lighting conditions and various vehicles. The proposed system mainly includes two procedures: license plate locating and character recognition. In license plates locating procedure, we convert the color image into grey level data first. Then apply the morphology technique to find out the location of license plate. The objective of the research is to increase the recognition rate of license plates and to improve the success rate of license plate locating. We demonstrate the feasibility of this system of this system through extensive experiments. The correct plate location rate and characters recognition achieves 96.63% and 93.20%, respectively.

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Tseng, Chien-hao, and 曾建豪. "Smartphone License Plate Recognition System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/36788628071050399131.

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Анотація:

碩士
國立中央大學
資訊工程學系碩士在職專班
100
In this thesis, we develop a license plate recognition system on Android smart phone. The proposed method consists of five stages: image pre-processing, license plate locating, orientation correction, character segmentation, and character recognition. The major properties are: (i) Segmentation of license plates is invariant to size of license plates. (ii) Segmentation of license plates is invariant to color of cars. (iii) The license plate extraction method is based on length/width proportion and color of license plates. (iv) Segmentation of license plates tolerates to the orientation variation of license plates. (v) Similar characters are recognizable. After bi-level thresholding, we use horizontal scan twice to segment license plates. First, the width of a license plate is assumed to be the same as the width of whole image in the first scan. Second, base on the aspect ratio of the license plate, the height found by the first scan can be used to get the ideal width for the second scan. Hence segmentation of license plates is invariant to size of license plates. This segmentation is based on license-plate characters other than plates; thus, the segmentation is not influenced by body color of cars. In addition to using the aspect ratio of license plates to filter, we also use license-plate background and character color to extract license plates. There are three steps in resolving orientation distortion of license plates. First, using average slope of upper and lower bounds of the license plate text to correct the orientation from pan rotation. Second, using license plate types to amend the total width of character blocks. Then, to find out the rotation angle that makes the license-plate height is largest, the number of connected blocks of characters is largest. At last, we use this angle to correct the tilt rotation of license plate images. Character segmentation is implemented by vertical scan. If the aspect ratio is wrong, the corresponding segmented block will be deleted, and then each real character block of the license plate can be retrieved. Character recognition is done by template matching. If the detected character is digit 0, letter O, letter D, and letter Q, or digit 8 and letter B, or digit 1 and letter I, we further use special character features to recognize again. The second recognition process can reduce the wrong recognition rate of these similar characters.

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21

D'Souza, Aaron, and Saprem Dalal. "Automatic License plate Recognition System." Thesis, 2011. http://ethesis.nitrkl.ac.in/2482/1/Automatic_License_plate_Recognition_System(107cs040%2C107cs030).pdf.

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Анотація:

Intelligence surveillance is an important commodity in traffic-based systems. Automatic License Plate Recognition (ALPR) is a challenging area of research. This work deals with problems related to artificial intelligence, neural networks and machine vision in the construction of an automatic license plate recognition (ALPR) system. This is done using mathematical principles and algorithms. These intelligent systems help in traffic monitoring during rush hours, road safety, commercial applications like in car parking lots and law enforcement. In this paper, a license plate recognition system is proposed which uses captured digital images of the rear or front of a vehicle and can be easily applied to commercial car park systems for access to parking spaces and also to prevent car theft issues.

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22

Kuo, Kuei-Lan, and 郭癸蘭. "Handwritten ID Number Recognition System." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/28798089241718556602.

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Анотація:

碩士
國立高雄第一科技大學
電腦與通訊工程系
90
This thesis brings up the implementation of handwritten ID number recognition system by the application of plastic perceptron neural network（PPNN）. The applied structure of PPNN in this thesis is improved from the learning algorithm and network structure of back-propagation neural network （BPNN）in artificial neural networks. The problems of traditional BPNN such as longer learning period, not prone to convergence, re-training while delete or add new patterns make the realization of real time BPNN system impossible. The proposed methods are combined with the parallel distributive process concept and modification of the BPNN structure could accelerate the learning speed and solve the re-training problem. The character segmentation, noise removal and extraction of feature are also discussed. Adequate extracted feature make recognition of character easier. The adoption of white run-length and pixel density could clearly display the structural and integral of the character respectively, and facilitate to make higher recognition accuracy.

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23

Chan, Yang-Kian, and 曾揚建. "A Research on Plate Localization, Preprocessing and Recognition of License Plate Recognition System." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/43798686065653582756.

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Анотація:

碩士
國立交通大學
電機與控制工程系
87
The license plate recognition system could be applied to various uses, such as parking lots management system, tolls of freeways, tracking stolen vehicles, etc. A complete license plate recognition system is integrated by five main procedures, including plate localization, image preprocessing, horizontal-axis calibration, vertical-axis characters segmentation and recognition. This Thesis focuses on plate localization, image preprocessing and recognition which have been analyzed and designed in detail. In the part of plate localization, we use image roughened technique to reduce image data and improve processing speed, then plate is localized after plate image is enhanced by a designed filter. From the experiment, this method has showed very successful performance about 98%. In the part of image preprocessing, we follow the character width to design a filter to enhance the part of characters, and binarize it to eliminate background noise. In the part of characters recognition, we use regions for gradient direction histogram calculations as feature, and adopt Back Propagation Neural Network (BPNN) to recognize numerals, alphabets and alphanumerals. Characters recognition rate is above 90% The techniques of license plate recognition proposed in this thesis have achieved industrial standards. However, we need to collect and improve the using problems under various environments.

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24

Wu, Meng-Tsung, and 吳孟璁. "Automatic Vehicle License Plate Recognition System." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/96706197081543255915.

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Анотація:

碩士
淡江大學
資訊工程學系
86
In this thesis, we develop an effective and fast automatic vehicle license plate recognition system. From a digital vehicle image without presetting the location and size of the license plate, the proposed system can automatically find the location of the plate, isolate the letters and digits occurring in the plate, and then recognize the characters. The system includes three parts: license plate location, character isolation, and character recognition. Images with 256 gray levels are used in this system. Firstly, vertical edges appearing in an image are detected by the techniques of digital image processing. Since the difference between the gray-levels of plate''s characters and those of plate''s background are significantly, it is easy to detect the edges of characters. By horizontal scanning, the lines with enough gray-level changes are preserved. Then possible locations of the plate could be identified from the larger areas based on the intervals in the vertical and horizontal projections. After locating the plate, the image of the plate area is converted into binary for accelerating recognition. Then the characters on the plate are isolated according to the vertical projection of the above binary image. Finally, the isolated characters are recognized by the moment method, which compares each character with standard character models by using the Euclidean distance. A postprocessing step is executed if errors exist. The experiment was performed on a Pentium personal computer. The experimental results show that 95 images whose license plates can be located correctly in 100 test car images, and each image needs approximately 0.3 seconds for locating the license plate.

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25

OU, YUAN-LIANG, and 歐原良. "A Portable License Plate Recognition System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37920587265579440474.

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Анотація:

碩士
國立臺灣科技大學
電機工程系
100
Nowadays, most researches on the license plate recognition system are implemented by computers with fixed cameras. However, most of them operate under restricted conditions, such as static background, fixed shooting angle, and limited number of cars. In this research, as few constraints as possible on the working environment are considered. Our study focuses on the development of a real-time multiple vehicles license plate recognition system on embedded devices with algorithms in low computational complexity and camera shooting angle in large range. It consists of four processing modules: plate localization, skew correction, characters segmentation, and character recognition. By the characteristics of high-contrast of plate image, the license plates are first localized. After detecting the skew lines, the skew plate images are corrected by geometric transformation. Then, applying projection method, the characters on each plate are segmented. Finally, a recognition algorithm by an adaptive template matching is applied to identify the characters on the license plate. The proposed algorithms are implemented on SIMIS BA-8 board. The major advantages of our system are that it accomplishes a multiple license plate detection and recognition system and it is a portable real-time device with large range in shooting angle. Our algorithm was tested with video streams in different shooting angles, distances, complex background, and variant illumination. The experimental results show that our proposed architecture is a flexible system. It can operate in real-time at a frame rate of 8 fps with good recognition rate in most circumstances.

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26

Song, Yong-Sheng, and 宋永勝. "License Plate Recognition and Management System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/aqrwhy.

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Анотація:

碩士
樹德科技大學
電腦與通訊系碩士班
106
In this paper, we design a license plate identification management system for the control of vehicles. In this system architecture, including a web page with a manager management function system, and a mobile APP that views and controls push notifications. We use a high-quality camera to capture the vehicle''s license plate and recognize the image alphanumeric, after identification, the data such as alphanumeric data is transmitted to the server side together with the captured image. The server software receives the data and compares it with the list of controled vehicles in the database, if it is confirmed to be a controled vehicle, it will be displayed on the webpage, and the notice will be pushed to all users who have previously installed the APP. The practical application of the vehicle management system is quite extensive., it can be used in the control of the parking lot of the residential building, the company''s public vehicle management and control, the police department''s inspection and management, etc., to improve the quality and protection of vehicle management.

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27

Lu, Chien-Te, and 呂建德. "Implementation of License Plate Recognition System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/ne2t59.

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Анотація:

碩士
國立臺北科技大學
電機工程系所
97
The purpose of this thesis is to explain how the indoor parking system uses the license plate recognition to shorten the user''s waiting time. License plate locating and segmentation of the plate characters are quite time consuming in license plate recognition. So this thesis presents high-speed License plate locating and segmentation of the plate characters methods. First, decrease and gray the license plate image which will decrease the operational volume. Then use Bottom hat transform to indicate the bottom value of the original image and locate the license plate. Use local average threshold to separate the license plate characters and license plate back ground. In the segmentation of the plate characters use vertical projection method to divide characters, then use character features to recognize each character. In order to verify the system feasibility. This thesis presents high-speed License plate locating and segmentation of the plate characters methods and implements the system at the NTUT underground parking system. The experimental results measuring up to less than the 500ms complete the license plate recognition norms of business, and the percentage of literacy rate is 90%.

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28

Chen, Ming-Yao, and 陳明瑤. "Handwritten Postal Zone Number Recognition System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/14182948799353541433.

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Анотація:

碩士
國立高雄第一科技大學
電腦與通訊工程所
91
In this thesis a handwritten postal zone number recognition system is proposed, based on the Plastic Perception of neural networks. The Plastic Perception Neural Network(PPNN) is based on the supervised learning of Back-propagation Neural Network(BPNN), and improved the traditional neural network which has the problems of time-consuming, difficult convergence and retraining of additional new patterns .In this thesis, we need some preprocessing technologies, such as noise elimination, text extraction and normalization. The feature extraction, in this thesis combine White Run-Length Coding and Pixel Density, to extract 65 elements of feature vector. From Two sets of samples with different quantity are trained and tested. At last, the postal zone numbers on the real envelopes are also examined. The satisfying recognition ratios are 88%, 95% and 92% respectively..

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29

Yang, Wen Tzong, and 楊文宗. "Automatic Vehicle Identification Number Recognition System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/16636917567719883726.

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30

Gupta, Nisha. "Automatic Number Plate Recognition Using Raspberry Pi2 in Shovel-Dumper Combination." Thesis, 2016. http://ethesis.nitrkl.ac.in/8318/1/2016_BT_112MN0606_Nisha_Automated.pdf.

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Анотація:

A designing of a system which captures the image of the number plate automatically of a Shovel-Dumper combination and these details were verified using Raspberry Pi2 processor for authentication. This system captures the number plate of shovel and dumper further processing for the character recognition. Automation is the most frequently spelled term in the field of electronics. The hunger for automation brought many revolutions in the existing technologies. This paper makes use of an onboard computer, which is commonly termed as Raspberry Pi2 processor. It acts as heart of the project. This onboard computer can efficiently communicate with the output and input modules which are being used. The device which is able to perform the task is a Raspberry Pi2 processor. When any vehicle passes by the system, the image of the number plate of every vehicle is captured using camera. The image of the number plate details are fed as input to the Raspberry Pi2 processor. The Processor takes responsibility to check the authentication details of every shover and dumper. Once the details are recognized then the processor operates it detects an unauthorized image of number plate was detected. To perform this task, Raspberry Pi processor is programmed using embedded ‘Raspbian’.

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31

Guo, Ming-Feng, and 郭銘峰. "An Efficiency Recognition System for License Plate." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/70870433128232365966.

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Анотація:

碩士
國立中興大學
應用數學系所
105
Although the popularity of transportation brings great convenience to people, it still causes a lot of problems. According to the statistics of motor vehicle registration from the Ministry of Transportation and Communications in Taiwan in 2017, there are increases and decreases in the number of motor vehicles, road traffic accidents, and road traffic offences. However, most of these traffic accidents and traffic offences are handled manually. For the sake of convenience, if these problems can be dealt with a complete system, a large amount of manpower in these cases can be reduced, personnel distributions and arrangements can be better. Therefore, in this essay, we propose an algorithm for recognition of license plates. First, we take out the part of the license plate in the images and we check whether the license plate is presented in a horizontal state or not. Next, we convert the images and remove the noise. Then we extract the characters and compare them with those in the database. At last, we obtain the results of comparison. For the images of the license plates that we use for testing, they were taken from open data on the Internet. In addition to standard images, there are images taken from different angles. People who do the research on recognition of license plates can find images that are suitable for testing on these websites. Compare with photos taken by ourselves, these images are more referential. Based on the results of the test, we have known that most of the images of the license plates can be identified. However, if the characters are obstructed, such as the blurs of the license plates and the noise generated when photographing etc., or the license plates incline too much, these factors will affect the process of identification.

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32

Chen, Hsiang-Chieh, and 陳翔傑. "Automatic vehicle license plate recognition system design." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26720851603323695840.

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Анотація:

碩士
國立中央大學
電機工程研究所
93
Recently, vehicle license plate recognition system plays an important role in intelligent transportation system. This paper will propose an algorithm with low computation and high recognition rate algorithm to realize a real time license plate recognition system. We divide our system into three stages, including license plate locating, characters segmentation and characters recognition. A novel method for extracting license plate in complex background is proposed. Due to the influence of lighting effects, tilt or dirty of the license plate, we also create a method to compensate these cases. In characters recognition, template matching and similarity measure are used such that our algorithm is more robust in different inclination and lighting conditions.

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33

Chen, I.-Chih, and 陳奕志. "Constructing Embedded Car License Plate Recognition System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/91717196909075534200.

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Анотація:

碩士
淡江大學
資訊工程學系碩士班
93
Embedded System is a computing platform designed for specific purpose. Because it’s task is much simpler than personal computer which is designed for general computing purpose, Embedded System can simplify its hardware architecture, cost down its hardware price, produce smaller device and low energy consumption. It also fits to be mobile computing platform. But Embedded System is constrained by its simple architecture; its processing power is much slower than personal computer. This paper purpose a software porting procedure between personal computer and Embedded System platform via a instance of Car License Plate Recognition System , and make Embedded Car License Plate Recognition System more efficient via exchange floating operation by integer operation and bitwise operation. For Example the image format captured by COMS, is Color Filter Array ,and this format will lost 2/3’s original image illumination. The illumination recovery process was originally involve with mass of floating operation, after applying the speeding method that we just mention before the processing time become times faster. We also use uClinux to assist hardware communication and process scheduling, and uClinux makes Embedded System able to handle complicated process control, also make software porting much smooth. Network File System ,NFS not only resolving the problem of lacking storage media, but also reduces the times of flash Rom burning procedure. Finally we use TCP/IP to transfer the image captured by CMOS to remote personal computer for Car License Plate Recognition ,and compare Car License Plate Recognition results between Embedded System and personal computer.

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34

CHIOU, CHIH-KUO, and 邱智國. "Implementation of A License Plate Recognition System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/dkfvjb.

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Анотація:

碩士
國立臺灣科技大學
電機工程系
95
The development of transportation is very important to a development country because the manipulation of economy need to depend it to propitious operation. Therefore, to build the Intelligent Transportation System (ITS) by means of electronic with information, network. The purpose of the ITS is to improve traffic efficiency and decrease traffic crowded and protect traffic accident. The thesis is to combine techniques of image processing and machine vision to build a license plate recognition system for ITS. The thesis propose a satisfactory strategic to improve license plate detection efficiency. By means of edge detection, binary, morphology and projection to detect the license plate and achieve pretty result. Experiments to detect license plate and character recognition in variable environment for distance, angle, indoor, outdoor and lights. The result is 92% for first license plate detection , 100% for more license plate detection and 92% for character recognition.

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35

Huang, Zhung-Long, and 黃正龍. "License Plate Recognition System of Dynamic Vehicle." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/81257750719474967974.

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Анотація:

碩士
國立中正大學
電機工程研究所
92
Design of a vehicle recognition system has been investigated for many years in the recognition field. It has been extensively and practicality used in many applications, such as parking lot auto management , automatic tollgate , traffic management , and searching stolen car, and so on. The system can be categorized in two methods, static and dynamic system. In early design many researchers emphasized static systems. First, the car must be parked at a suitable position. Then, the car license plate need to be compensated for the brightness and made some pre-processing in order to emphasize constract. Finally, a license plate image is taken under various restricted conditions for obtaining an image fit for processing. If the vehicle is not parked at a suitable position, car parking is need to do again. It would make the driver very inconvenient.However, the static system makes the question be solved more identically and simply because of some premotioned restricted conditions. Therefore, the process of static system is easier than dynamic system. On the dynamic system, vehicle and background are unnecessary to make any restrict. The Dynamic system uses video camera to catch a sequence of frames with various positions and angles of the moving vehicle. Therefore, the dynamic system can obtain more information from the frames. Thus, a car can be recognized more times to increase the probability of recognization, since the images are catched during a continuous time period. Conclusionly, the system can more adapt to operation in the life.

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36

HAO, HSU-CHIA, and 許家豪. "New Vehicle Plate Localization and Recognition System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70089951368260996406.

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Анотація:

碩士
國立臺灣科技大學
電子工程系
96
A rapid growth of the numbers of automobiles in Taiwan . According to the Ministry of Communications of Republic of China’s report in April, counted the motor vehicle quantity is 20,840. The associated problems with the rapid growth of vehicles such as traffic violation, stolen vehicles, and so on, present great challenge to the government. Currently, the most important task in transportation controls is the automobile license recognition. For instance, if monitoring point can be set up in all public places, then parking lot management, typical road, bridge booth, and etc then efficiency in human resources can be improved as license plate record can be managed automatically. In the parking lot automation management, it is a must to be able to distinguish different vehicles’ license plate by recognizing the English numeric code. This thesis discusses mainly on the develops an effective license plate recognition system for parking lot automation management. If we take the pictures outside, it is also known for their complex background objects such as trees, billboards, traffic signs, etc. In this study, a system is proposed for multi-target vehicle license recognition. The recognition of English and the numeral is the key for parking lot automation management, as it replaces manpower. This developed of car license recognition system can be divided into three parts. The first part is the car license localization, the second part is the cutting of character Yuan, and lastly it is the recognition of the character Yuan. In the mean time, the utilization of existing Logitech Webcam and PENTAX the S4 in laboratory under perfect weather, stochastically picks up the vehicles car license in the school second parking lot which the intercourse and parks, and composes the formula union hardware using Borland C++ to carry on image processing which each kind of vehicles localization recognizes.

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37

Huang, Pin-Kai, and 黃品凱. "License Plate Recognition System Using Scattering Transform." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/50371626420278519354.

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Анотація:

碩士
國立暨南國際大學
資訊工程學系
104
In this thesis, we proposed a new method for recognizing the Taiwanese car license plates issued since 2012.The proposed method consists of a license plate detection step, a license plate rectification step, a character segmentation step, a feature extraction step, and a character recognition step. The license plate detection step is accomplished by using the OpenALPR, an open source license plate recognition software package.The four corners of the detected license plate are used to compute a homography matrix to rectify the license plate image. A model-based character segmentation method is developed to obtain single-character images. Features of the character images are extracted with scattering transform. Characters are recognized using the support vector machine (SVM). In order to train the SVM, a license plate image synthesis system is implemented to provide the training images. The image synthesis system generates license plate images corrupted by several effects such as out-of-focus blur, shadow, image noise, and character deformation due to the license plate localization error. Real experiments show that the average accuracy of the proposed method is 98.82%. It even outperforms the convolutional neural network approach in digits recognition.

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38

Feng, Hsin-Tsung, and 馮信璁. "Embedded System Implementation for License Plate Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71762219451264254073.

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Анотація:

碩士
元智大學
電機工程學系
98
In this paper, we present a license plate recognition system in the embedded system platform. Our system employs the Ada-Boosting technique to train the model in order to detect the license plate. After the license plate is detected, this system will segment the license plate into some characters and will extract the features. Subsequently, we use the SVM classifier to classify the feature classes so as to recognize the accurate character’s meaning. Finally, these meaningful characters are checked by the mechanism of syntax analysis in order to differentiate the false results. Our embedded system DaVinci 6446 platform is composed of the ARM and DSP units, which is manufactured by the Texas Instruments. DaVinci 6446 platform is a two-core processor. ARM is responsible for the system control and DSP processes the complex mathematical operation. By the cooperation of the two cores, the performance can achieve real-time processing. Our system obtains an average processing time of 41ms per frames, about 25 fps.

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39

Su, Tsung-mao, and 蘇琮貿. "License Plate Recognition System based on FPGA." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/64871133549872386168.

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Анотація:

碩士
國立臺灣科技大學
電子工程系
100
License plate recognition system has been widely researched and developed in the academic sphere and commercial area. The license plate recognition consists of three parts including license plate detection, character segmentation, and character recognition. License plate position uses image processing techniques which include grayscale, thresholding, mathematical morphology, and monnected-component labeling. These image processing have to accessed the neighboring pixels frequently to achieve intended mathematical formulas. To reduce the image data transfers, recently accessed pixels are stored in the register on FPGA. This leads to consume a large number of FPGA memory and logic resource in synthesizing our design, but image processing of proposed pipeline architecture on FPGA drastically decreases the amount of times of read and write to memory. The hardware based image processing system shows great speed up in the license plate recognition . The proposed license plate detection applies Grayscale and Threshold. It uses important contrast features for plate object detection. Applying grayscale to the color input car image will filter the plate object. License plates in Taiwan are not only white background; they also can be red, green and yellow background. Except white, the other background colors of license plates are difficult to be located. The threshold method of this paper uses saturation, intensity and hue (HSI), to easily find attribute of the color plate and makes the plate location a precise task. Character recognition of license plate recognition system uses neural network, syntactic structural, template matching and fixed-point sampling method. This paper applies neural network for character recognition. It is highly accurate and can be structurally designed on FPGA with great performance. Our experiment shows the proposed FPGA-based license plate recognition system achieves 98.18% license plate detection rate, 96.27% character segmentation rate, and 97.09% character recognition rate.

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40

Chen, Kai-Min, and 陳楷旻. "Shadow Removal Method for License Plate Recognition and Its Android Electronic License Plate Recognition System Implementation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/96096944989924891612.

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Анотація:

碩士
國立雲林科技大學
電機工程系碩士班
101
In License Plate Recognition (LPR) system, License Plate Detection (LPD) and binarization are both key image processing steps. But, under indoor or outdoor scene environments, LPD and binarization are both error-prone to the shadow interference caused by the shadow occlusion on the license plate scene images. Especially for diverse applications of potable and fixed LPR systems, shadow occlusion and interference issue resulting from uneven lighting condition is always necessary to be overcome. So this thesis proposes two shadow removal methods to solve the shadow interference issues on LPD and binarization, respectively. This thesis not only proposes Simplified Dynamic Range Optimization (SDRO) method based on Gamma Correction to fade the shadow occlusion issue at the stage of LPD, but also proposes Edge-Density-Partitioned Binarization (EDPB) shadow removal method to eliminate the shadow interference issue at the stage of binarization. Experimental results show that the proposed SDRO and EDPB shadow removal methods can perform the best improvement on the LPD rate and optical character recognition (OCR) rate, respectively, for shadowed indoor and outdoor vehicle, automobiles and motorcycles, license plate scene database. Besides, two proposed methods both take relatively less computational time. On the other hand, this thesis implements “portable and fixed electronic license plate recognition device”, and integrates it with a self-built “application-specific remote license plate database” for accomplishment of stolen vehicle investigation application or drive-through commercial service. The embedded implementation can verify and enlarge the reliability and practicability of LPR technology.

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41

Jong-Jyh, Tash, and 蔡宗志. "Character Partition. Normalization and Recognition Research of License Plate Recognition System." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/15381096599400703564.

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Анотація:

碩士
國立交通大學
電機與控制工程系
87
On the increase of car, so how to use automatic license plate recognition system for car''s conservation is important. Although the history of license plate recognition system research is long, the system still has many faults. In our study, we develop a license plate recognition system whose hardware and software update conveniently. The research of thesis focuses on Character Partition Horizontal Normalization and Recognition. The purpose of Horizontal Normalization is to cut the region that is out of the character horizontal edge. And let the horizontal of character are the same. After this process the character partition and recognition is simple. On the character partition research, we use two methods. One is using template map to partition the character of license plate. The other is using growing region method to partition the character. Both of those two methods have advantage and disadvantage. The result of our system is using growing region to partition the character of license plate. Because of this method has batter adaptability and correct percentage. Recognition is the last part in our process. Because of the size and shape of character is almost the same, so we use statistics to create the stander map character, and then recognition these character with map. But this method does not recognize the shape similar group like O. D. Q and 0. So we add shape and other particularity to redouble recognition. In our study the testing samples include 362 pieces of license plate image those were come form realistic parking lot''s monitorial system, wherefore our experimental inference is dependable and our implement system is realizable.

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42

Ting-HuaHsu and 許庭樺. "Applying Deep Learning in License Plate Recognition System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/80569052251254235815.

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Анотація:

碩士
國立成功大學
資訊工程學系
104
License Plate Recognition (LPR) is a very important part of an intelligent transportation system. The LPR is broadly used in many applications, such as highway toll station and car park management. From 2012, Directorate-General of Highway (Taiwan) pursues a new type of license plate, which is changed with the type of font and extra one more digit than existing license plate. In order to solve the recognition on the new license plate (7 digits) and existing license plate (6 digits), we need to redesign a model to made these two types of license plates can be completed in a single system. In this work, we implement a LPR system based on the neural network to achieve 6 digits and 7 digits’ license plate recognition. The neural network training part is completed with an open source tool, Tensorflow. The results show that our LPR system obtains high accuracy rate even the characters are defaced.

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43

Wang, Ching-Chung, and 王精忠. "THE STUDY OF CAR LICENSE PLATE RECOGNITION SYSTEM." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84668349117339820560.

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Анотація:

碩士
大同大學
通訊工程研究所
93
Along with economical grow up and commerce activity vigorous development, people for the automobile need is more and more, although government for the traffic construction is very popular, but in the crowded Taiwan area, the question of parking space not enough is a fact of without saying, so how to manage parking lots efficiently and increasing usability of the parking lots that is our concerned question. This thesis proposed the license plate recognition system, includes license plate locating, image binarization, calibration of license plate, character segmentation, character recognition and so on, total five parts; In the license plate locating, we use the image process technique to process the input image of automobile change into fixed resolution gray image, use again Sobel edge detection method to find out the edge of license plate, at last use filter to find out the position of license plate; In the image binarization, we use dynamic threshold value method to find out threshold value, let gray image of license plate change into binarized image; In the calibration of license plate, we use bottom outline of license plate analysis method to find out slope angle of license plate and to execute calibration; In the character segmentation, we use vertical projection method to find out the high of character, and we use horizontal projection method to segment the characters of license plate, at last we use partial recognition method to recognize the number of license plate image. This system takes 200 license plate images from indoor and outdoor parking lots to execute the experiment of license plate recognition, experimental results, the license plate locating successful rate is 98%, the character segmentation successful rate is 95%, the character recognition successful rate is 93%, the average recognition time of each image needs 1.2 second.

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44

Lue, Hsin-Te, and 呂信德. "Recognition System of License Plate Using Multi-Experts." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/11971891100975928632.

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Анотація:

碩士
國立中央大學
資訊工程研究所
90
The development of transportation is one of very important strategies in a developing country because the manipulation of economy, politics, and society has to rely it to normally operate. Therefore, modern countries start to investigate how to combine electronics with information, communication and network technologies to build the Intelligent Transportation System (ITS). The purpose of developing ITS is to improve traffic efficiency and environmental protection. The goal of this thesis is to use the automatic technologies of image processing and computer vision to develop an automatic license plate recognition system with an eye to establishing the foundation of intelligent transportation management. In the proposed license plate recognition system, rule-based technique is first employed to locate license plate. Then, template matching is adopted to extract character image. Last, multiple experts is applied to construct main recognition module for increasing the character recognition rate. In the recognition module, local and global statistic features are extracted by devised methods. These features are then fed to the two experts recognizers. The two experts operate parallelly to recognize the character image. The decisions are combined to yield better recognition result. Experiments are conducted on which were vehicle images taken from highway day and night. Hence, the brightness of images will not be the same. For this reason, it is impossible to handle all images by a single threshold. However, the license plate locating rate, character segmentation successful rate, and character recognition rate in our system are all over 97%, and the whole recognition rate is 92.8%.

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45

Chang, Wei-Shang, and 張瑋珊. "License Plate Recognition System for Moving Video Streams." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/k8spz8.

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Анотація:

碩士
國立臺灣科技大學
電子工程系
99
License plate recognition systems have been developed for many years. Most of them are installed with video cameras at fixed locations such as highway toll stations or parking lots. However, very few studies have focused on license plate recognition system with moving camera. In Taiwan, the density of vehicles is ranked top in the world. Taiwan is also known as the highest motorcycle-density country in the world. Presently the police investigating stolen vehicles use system by manually entering license plate numbers to check if they are stolen vehicles or not. In this thesis, we develop a plate license automatic recognition system with moving cameras. This allows polices to record the car or motor-cycle plate numbers while patrolling in their cars. This system comprises the following modules: image pre-processing, license plate localization, plates processing, and optical character recognition. First, pre-processing module is used to converts color input video into 8-bits grayscale images. Next, license plate localization module uses Sobel edge detection operator to find strong vertical texture and locates the license plate area in images. Then, the plate-processing module uses adaptive threshold processing to get more accurate area of license plate. In this module, if the license plate characters are white, they will be transformed into black characters. Finally, optical character recognition module is used to recognize the character. We have adapted the Tesseract-OCR engine to do the task. We use captured plate charter templates to train its characters database and get good recognition result.. Experimental results show that our system has 98.61% successful license plate areas localization rate. It achieves 90.52% successful license plate recognition.

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46

Wu, Yi-Hsuan, and 吳宜軒. "A Vision-based Car License Plate Recognition System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/93542526634672435652.

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Анотація:

碩士
亞洲大學
資訊工程學系碩士班
97
This paper presents a license plate recognition system based on the computer vision, including five parts which are image pre-processing, license plate locating, character segmentation and recognition. In the image pre-processing part, the RGB images obtained from the camera are changed into the gray images. After the gray images are changed into the binaries images, and the noises are filtered by using the median filter. In the license plate locating part, we predict the possible location of the license plate in the binaries image and acquire it with using the length and width value of the general license plate. In the character segmentation part, the characters of the license plate are segmented due to the high contrast between those and the background. At last the neural network is adopted into the pattern recognition, and the letters and numbers could be recognized. Moreover, we exploit the Bottom-hat module to enhance the features of the license plate in order to promote the identification significantly.

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47

Kao, Sho-tsung, and 高碩聰. "An autonomous license plate detection and recognition system." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/72090163125968579734.

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Анотація:

碩士
國立臺南大學
數位學習科技學系碩士班
96
The paper proposed an autonomous license plate detection and recognition system with computer vision. The system consists of four subsystems: car detection subsystem, plate extraction subsystem, character division subsystem and character recognition subsystem. Car detection subsystem uses MMADR and NDDR of dynamic image to find the location of the cars on the screen. Plate extraction subsystem uses the characteristics of the plates and algorithm used to search plates to extract plate; character division subsystem combines Tophat, Labeling and LRE to automatically divide. As to character recognition subsystem, after comparing identification effects of SVM and BPNN, we choose BPNN as the recognizer. Experiment outcome proves that our system can effectively detect cars and recognize the plates under different lights.

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48

Huang, Ching-I., and 黃錦溢. "Design of the Vehicle License Plate Recognition System." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/64503660703529505480.

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Анотація:

碩士
淡江大學
電機工程學系
88
The main idea of the thesis is to realize a system of vehicle license plate recognition which is effective and fast-calculating. It can be widely used in the management of parking lot , automatic fee charge and checking on stolen vehicle. Because of the variation on environment is large and the recognized vehicle is not easy to be controlled, so we have to look into a recognition system widely suiting for each state. In this thesis, we use two steps of sliding window to locate speedily the demarcation of the Vehicle Identification Number (VIN) according to the regulation of license plate, then use the partial linear gray level transformation to enhance the contrast and the recursive K-mean binary method in combination with projection technique to segment the VIN. Finally, the directional features of 80 dimensions are extracted and the back-propagation neural networks is used for obtaining the final solution. The experimental data of the system is to take a picture outside in the morning, noon, and evening respectively, and also in an underground parking lot. There are 600 pieces of pattern in which the resolution is pixel without any additional illumination. The experimental result shows that our method has great effect in application and the recognition time of each image is about 0.45 second.

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49

Yang, Chih-Chiang, and 楊志強. "Automatic License Plate Recognition System for Patrolling Vehicles." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/05839643474555607012.

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Анотація:

碩士
中原大學
資訊工程研究所
102
License plates are considered the first important information for vehicle-related crime investigation (e.g., vehicle theft, etc.). Conventional investigation requires the investigator (policeman) to manually enter the license plate number for suspicious vehicles which remains tedious and labor-intensive. The objective of this study was to develop an Automatic license plate recognition system for patrolling vehicles. We explored the idea to provide an automatic system by installing a surveillance camera (e.g., a vehicle video recorder) on a patrolling vehicle (e.g., police car). The system can be described in two phases, namely hardware configuration and software development. Technical approaches included: License Plate Localization, License Plate Correction, Character Segmentation, and Character Recognition. Overall, our system could achieve the license plate localization and character recognition of over 90%. In summary, our system could be incorporated in an integrated system with wireless communication for querying the vehicles’ information to assist the vehicle-related crime investigation.

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50

彭裕航. "Automatic Recognition System of the Vehicle License Plate." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/30879426620739533634.

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Анотація:

碩士
中華大學
機械工程學系碩士班
96
In this dissertation, we construct the License Plate Recognition system by using PC as a platform and image process techniques. A new character structure hierarchy is proposed and the correlated character characteristic value and character coding are defined. Also, character thinning processing is applied in the system to identify the English characters and digits on the car license plate. 　　The experiments of this system include two parts. The first part is to locate the license plate in images and divide characters on the plate. By using the following technique, such as Sobel edge detection, noise filters, threshold, and projection, to the captured images; characters and dash on license plate are divided. The second part is character recognition. Characters are recognized by determining the character end-point position, direction of end-point, four corner location, and vertical as well as horizontal characteristic categorization. The experiment results demonstrate that the system attains satisfied performance for recognition speed and correctness rate.

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