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1
Colberg, Kathryn. "Investigating the ability of automated license plate recognition camera systems to measure travel times in work zones." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49048.
Повний текст джерелаАнотація:
This thesis evaluates the performance of a vehicle detection technology, Automated License Plate Recognition (ALPR) camera systems, with regards to its ability to produce real-time travel time information in active work zones. A literature review was conducted to investigate the ALPR technology as well as to identify other research that has been conducted using ALPR systems to collect travel time information. Next, the ALPR technology was tested in a series of field deployments in both an arterial and a freeway environment. The goal of the arterial field deployment was to evaluate the optimal ALPR camera angles that produce the highest license plate detection rates and accuracy percentages. Next, a series of freeway deployments were conducted on corridors of I-285 in Atlanta, Georgia in order to evaluate the ALPR system in active work zone environments. During the series of I-285 freeway deployments, ALPR data was collected in conjunction with data from Bluetooth and radar technologies, as well as from high definition video cameras. The data collected during the I-285 deployments was analyzed to determine the ALPR vehicle detection rates. Additionally, a script was written to match the ALPR reads across two data collection stations to determine the ALPR travel times through the corridors. The ALPR travel time data was compared with the travel time data produced by the Bluetooth and video cameras with a particular focus on identifying travel time biases associated with each given technology. Finally, based on the knowledge gained, recommendations for larger-scale ALPR work zone deployments as well as suggestions for future research are provided.
2
Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.
Повний текст джерелаАнотація:
In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process.
Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%.
After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably.
The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features.
Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%.
The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulate the process of vehicle retrieval.
3
Luvizon, Diogo Carbonera. "Vehicle speed estimation by license plate detection and tracking." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1380.
Повний текст джерелаАнотація:
CNPqSistemas de controle de velocidade são utilizados em vários países para fiscalizar o cumprimento dos limites de velocidade, prevenindo assim acidentes de trânsito. Muitos desses sistemas são baseados em tecnologias intrusivas que requerem processos de instalação e manutenção complexos, geralmente atrapalhando o trânsito. Neste projeto, propõe-se um sistema não intrusivo para estimativa da velocidade de veículos baseado em vídeo. O sistema proposto detecta veículos em movimento utilizando um detector de movimento otimizado. Aplicou-se um detector de texto especializado para localizar a placa dos veículos, a qual foi utilizada para seleção e rastreamento de pontos estáveis. Os pontos rastreados são então filtrados e retificados para remoção do efeito da perspectiva. A velocidade dos veículos é estimada comparando-se a trajetória dos pontos rastreados com dimensões conhecidas no mundo. Para os testes, utilizou-se aproximadamente cinco horas de vídeos em diferentes condições, capturados por uma câmera de baixo custo posicionada a 5,5 metros de altura. Os vídeos capturados contém mais de 8.000 veículos distribuídos em três pistas diferentes, com as velocidades reais para cada veículo obtidas a partir de um detector por laço indutivo. O detector de placas proposto foi comparado com três outros métodos no estado da arte e obteve os melhores resultados de performance para os nossos vídeos, com precisão (precision) de 0,93 e coeficiente de revocação (recall) de 0,87. A estimativa de velocidade dos veículos apresentou erro médio de -0,5 km/h, permanecendo dentro da margem de +2/-3 km/h, determinada por agências reguladoras em vários países, em 96,0% dos casos.
Speed control systems are used in most countries to enforce speed limits and, consequently, to prevent accidents. Most of such systems are based on intrusive technologies which require complex installation and maintenance, usually causing traffic disturbance. In this work, we propose a non-intrusive video-based system for vehicle speed estimation. The proposed system detects moving vehicles using an optimized motion detector. We apply a specialized text detector to locate the vehicle’s license plate region, in which stable features are selected for tracking. The tracked features are then filtered and rectified for perspective distortion. Vehicle speed is estimated by comparing the trajectory of the tracked features to known real world measures. For our tests, we used almost five hours of videos in different conditions, captured by a single low-cost camera positioned at 5.5 meters height. The recorded videos contain more than 8,000 vehicles, in three different road lanes, with associated ground truth speeds obtained from an inductive loop detector. We compared our license plate detector with three other state-of-the-art text detectors, and our approach has shown the best performance for our dataset, attaining a precision of 0.93 and a recall of 0.87. Vehicle speeds were estimated with an average error of -0.5 km/h, staying inside the +2/-3 km/h limit determined by regulatory authorities in several countries in over 96.0% of the cases.
4
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.
Повний текст джерелаАнотація:
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.
5
Kao, Kung-Chun, and 高孔君. "License Plate Detection on Autonomous Surveillance System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/64794522132045066604.
Повний текст джерелаАнотація:
碩士玄奘大學
資訊管理學系碩士班
98
Autonomous surveillance systems are widely used as an important tool for security control in public areas. Among the numerous targets of the autonomous surveillance system, license plate recognition can help to identify the terrorist in cars. Uneven lighting conditions happen all the time in an autonomous surveillance system. As a result, traditional license plate detection can’t achieve the goal. In this research, we propose a novel method for license plate localization. The features of a license plate is generated in preprocess through the morphology. Then, apply the AdaBoost algorithm to select some weak classifiers from the weak classifier space to construct a strong classifier. Experimental results show that the proposed method can efficiently detect license plates under different illuminations.
6
Tseng, Wei-Chung, and 曾瑋中. "License Plate Detection System of Low-resolution image." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/67347438421408621209.
Повний текст джерелаАнотація:
碩士國立雲林科技大學
電機工程系碩士班
92
The amelioration of social order in our country caused by gun-fire event, extortion of car and human hostage, and robberies has greatly influence the society. This crisis has made security check and video surveillance become more important. Currently, the installation of video surveillance cameras is getting popular on the streets of many counties and cities. The recording of the passing vehicles and their license plate in these videos have successfully help the police solving many cases including hit-and-run, extortion of human hostage, etc.. However, search the video to extract and record the license plate (LP) of the suspected vehicles needs a lot of effort and time. This tedious work sometimes decreases the willingness and efficiency of the police in pursuing the criminals which places a great threat to our society. It''s obvious that the police needs an automatic system to extract the license plate from the video for them. In view of the above demand, we propose utilizing the techniques of image processing and develop an automatic LP detection system to suit the police department''s urgent need. Although most of current video cameras are equipped with a resolution of 640x480, to save the storage space, videos are usually taken at low resolution (320x240) and saved at high compression ratio. This available low resolution and poor quality data not only distinguish the research in this thesis from that of the other LP detection system, it also puts a great challenge to our design. In this thesis, we deal with both daytime and night-time video. On the basis of local variance of the image and techniques of morphology (e.g. bottom-hat, top-hat), method which detect location of the license plate was successfully developed. Videos of 12-hour daytime and 2-hour night-time taken by police department from several street scenes are used to evaluate the system performance. The results show that the LP detection rates are 93.74% and 78.57% for the daytime and night-time, respectively. Their corresponding false alarm rates are 1.17% and 15.58%.
7
Kao, Sho-tsung, and 高碩聰. "An autonomous license plate detection and recognition system." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/72090163125968579734.
Повний текст джерелаАнотація:
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.
8
Yao, Chou-Yang, and 姚州陽. "License Plate Detection System Implementation by C Language." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/09082221285429681040.
Повний текст джерелаАнотація:
碩士中華技術學院
電子工程研究所碩士班
98
The purpose of this research was to implement License Plate Detection System by C Language, in which MathWork MATLAB 7.6 and Microsoft Visual Studio 2005 were main development tools. Mex-function was implemented in C Language since it is essentially famous for efficiency, structure, portability and good readability. This study tries to take advantage of MATLAB API to dynamically link MEX library in run-time. The goal is turning script architecture (M-files) into Mex-functions. Programming process adopted a progressive approach to retain the benefits of MATLAB workspace, which means MATLAB built-in instructions are gradually replaced step by step. The method is able to easily verification and evaluation the performance by MATLAB’s friendly user interface. Photos taken from the digital camera provide the input images to the system in which environmental conditions could be day or night without external light source. Default accepted resolution is 1024x768 pixels. Experimental results showed that as long as the pixel numbers of license plate close to assumed 60x220, the characters could be recognized successfully. With appropriate tunned parameters, it can support more image sizes and variety of different plate pixels. The final system output is an image that re-combinated by the prefabricated digit/alphabet pictures according to the characters identification result from original images passed through processes of the license plate location, template matching and some pre-treatment.
9
Hsu, Ren-Wei, and 許仁瑋. "A Real-Time System of Multiple License Plate Detection." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/22040438312555513397.
Повний текст джерелаАнотація:
碩士國立中興大學
電機工程學系所
102
License Plate Recognition (LPR) has recently played a critical role in applications of Intelligent Transportation System (ITS), such as parking spaces management, records of traffic violations, and searches for stolen cars. However, although research on license plate recognition has been undertaken over many years, in practical applications, the accuracy of detection and identification can decrease because of the complex backgrounds and light changes in capture image. Therefore, recognition systems should be limited to specific environmental settings to obtain favorable results. In summary, interference caused by the environment is the main factor that limits the development of LPR. LPR and License Plate Detection (LPD) comprise two main steps, character segmentation and character identification. The license plate zone is detected prior to the two steps, and is the key to successful recognition of the license plate zone. Most LPR studies have investigated static and low-resolution images. Conversely, the present study involved using 720P high-definition video and a PCIe (PCI Express) image capture card to detect license plates. Subsequently, to achieve real-time LPR, Haar Wavelet Transform was applied in extracting an HL image, reducing the algorithm calculation time. Chapter 1 provides a review of relevant literature, and introduces the system architecture developed in this study. Chapter 2 introduces related techniques, including color space transform, motion detection, image sharpening, histogram equalization, Haar wavelet transform, edge detection, binarization, and connected component labeling. Chapter 3 describes the algorithm used for LPD in detail. First, the motion object is detected. The license plate features are then intensified, and adaptive thresholding is conducted to binarize the image. The edge is then connected, and connected component labeling is used to label each block. Thus, the license plate location is obtained. Chapter 4 introduces the experimental equipment and software interface used. In addition the experimental results obtained using the aforementioned algorithm in this study are described. In the final chapter, a conclusion is presented and possible methods of the amendments are suggested in accordance with the experimental results.
10
ANAND, SHUBHAM. "DESIGN AND EVALUATE LICENSE PLATE DETECTION SYSTEM BASED ON SEGMENTATION." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18364.
Повний текст джерелаАнотація:
A continual upsurge in the volume of vehicles has been noticed over the past few decades
with the increase in population all over the world. Therefore, tracking of vehicles depending
upon the number plates is crucial to guarantee the control of vehicular traffic in competent
manner. The vehicles can be detected on the basis of their tags with the help of a new image
processing-based technology referred as ANPR (Automatic Number Plate Recognition) the
expertise is ahead of time ubiquity to ensure security and traffic management. This system
makes use of computer vision approach for extracting information regarding the abnormal
state from a digital image using a computer. Almost all number plate localization algorithms
combine many processes that result in a long computational time. Most of the image details
are lost or image quality gets degraded as a result of complex, noisy content in images. The
non-consistency of processes cause degradation which in turn affects the image quality. The
car number plate detection has many stages. In this research work, technique of voting
classifier is used for detecting the number plates of cars. For the purpose of voting
classification, we have used a unique combination of classifiers. The voting classification
proposed in this research work for the number plate detection is the combination of SVM and
random forest classifier. The MATLAB and/or GNU Octave has been used for the evaluation
of the proposed model. The efficiency of new algorithmic approach is examined with respect
to accuracy, precision and recall. The proposed algorithm gives accuracy up to 95 percent
for the car number plate detection. Similar, observation with the Precision and the
Recall that comes out to be 95.81 percent and 95.45 percent respectively.
11
Chen, Hong-Hsiang, and 陳泓翔. "License Plate Detection and Character Segmentation System Base on SoPC." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/74594443176139988758.
Повний текст джерелаАнотація:
碩士國立臺灣科技大學
電子工程系
97
In recent years, research and development of automated license plate recognition system in terms of car parking charges or traffic ban. this paper presents the implementation of a real-time FPGA license plate recognition system, and we achieve the detect license detection plate hardware base on SOPC, we got good performance of real-time detection .In this paper, the license plate detection we achieve by using discrete wavelet transform(DWT) and morphology. Furthermore the object Connect Component Labeling method to segment each independent block and identify the images license plate location coordinates. Before we get the characters of each registration coordinates, we should segment each region of the characters from the plate. The character image will be send to character recognition software (OCR). After that, the license plate characters identification will be finish. Using above method, we can achieve this purpose on Terasic company’s DE2-70 FPGA development kit, which compose of Altera NIOS II soft core embedded processor and SOPC platform development environment. And it combined with camera whose resolution is 720X487.After the system finished the plate detection and cutting out characters, it shows the result from VGA interface to the Monitor. Finally, we can successfully capture the license plate on the rate 96% and 85% on segment the characters, the constrained of our environment are the following: the vehicle 1 to 3 meters, the license plate can’t slope over 5 degrees, the region had no any obviously shelters or dust. If on the other environment, even if the original image slightly tilted or have different light intensity. According to the experiment, we can still reach 80.5% success rate of license plate detection.
12
Sung, Ming-Che, and 宋明哲. "Implementation of License Plate Detection and Recognition System on PDA." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/61703338713751414744.
Повний текст джерелаАнотація:
碩士元智大學
電機工程學系
97
This thesis mainly aims at discussing how to utilize general handheld PDA equipment, detect and recognize to the license plate of the image. The system is developed on Windows Mobile platform. Seeing that the treatment technology of the image is mostly developed on the PC platform, it is unable to make the system miniaturize, melt and save the cost lightly. The purpose is that hopes through the developing tools of Microsoft and cooperates with PDA equipment, easy to use and convenient characteristic transmitted realize that the license plate detection and recognition system. The principle of our system is made up by follow steps, i.e., use PDA capture the picture, pre-processing image, detect and recognize license plate, post-processing image, and finally deliver the result to screen.
13
YANG, ZUO-RONG, and 楊祚榮. "A Real-time License Plate Detection System Based on FPGA." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/86371055107267231251.
Повний текст джерелаАнотація:
碩士國立臺灣科技大學
電機工程系
102
License plate detection system is a key technology in many applications such as license plate recognition, and front vehicle distance estimation system. Front vehicle distance estimation system can assist drivers’ safety. The system which is designed to assist drivers’ safety is called Advanced Driver Assist Systems (ADAS). For safety reason, it must be stable and real-time. Many algorithms are developed to overcome environment variation. However, the more complex algorithms are, the more difficult to implement on hardware. Up to now, most of the front vehicle distance systems are implemented on PC or embedded system. In this thesis, we proposed a real-time license plate detection system based on hardware design to enhance the processing time and apply to front vehicle distance estimation system. There are five basic steps in our processing system: (1) Image cropping, (2) Noise removal, (3) Labeling, (4) License plate feature extraction, (5) Localization. Each step is designed by hardware circuit module written in Verilog HDL. Finally, the proposed hardware architecture is implemented on Altera DE2-70 development board to verify the feasibility of our hardware design. To implement our system requires 36,782 logic elements. Except for input and output modules, no more SDRAM is needed to store the image data. Instead of large amount of SDRAM, only one line buffer (320x1) and several registers are required in our design. Thus, the cost of our implementation can be reduced. It can operation in real-time at a frame rate of 30fps. The experimental result shows our proposed license plate detection architecture attains a real-time reliable system with a high detection rate 98.4% in general environment and 83% in most of environments.
14
Ho, Li-Kung, and 何立功. "Implementation of License Plate Detection and Recognition in Embedded System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/82718990873073525284.
Повний текст джерелаАнотація:
碩士元智大學
電機工程學系
96
A procedure is using Ada-Boosting technique for license plate detection and recognition to develop on hardware platform. According to recent techniques of video processing are almost developed on PC platform, it is heavy, costly, and no space to reduce size of system. However, there are coming more and more embedded systems which are best and best in the future. The goal is making system faster, smaller and cheaper. Our purpose is to develop a license plate system on car and implement it on DSP based hardware system. The principle of our system is made up by follow steps, i.e., capture video, pre-processing image, detection, recognition, post-processing image, and deliver video.
15
Chen, Chia-Hao, and 陳家豪. "Kinect-Based Static/Dynamic License Plate Detection and Recognition System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/62902829973826480118.
Повний текст джерелаАнотація:
碩士淡江大學
電機工程學系碩士在職專班
100
In this paper, we divide the system into three parts: the extraction of the depth information, the orientation of the license plate and the identification of the words. First, we exact the depth information of the license plate on the motorcycle in front of the Kinect to solve the problem of the complex background. Then we use the algorithm to divide the image of the motorcycles sequentially and abstract the image of one motorcycle to do the following process. After that, we apply the Marker Detection Algorithm with the characteristic of searing the square object to orientate the license plate. The algorithm can not only detect in the real-time but also have the robust resistance to rotation, deform, slope and shelter, etc. Finally, we correct the slope of the retaining license plate and divide the words. We base on the sample to identify the words. The training motorcycle license plates divide into the static and dynamic state. The dynamic state is to detect the driving motorcycle license plate under moving. But, the static state is to detect the parking motorcycle in the roadside under moving. However, our system’s main purpose is the static detection.
16
Wu, Chun-Yen, and 吳俊諺. "Image Motion Object Detection for License Plate and Gesture Recognition System." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/52083337034293954050.
Повний текст джерелаАнотація:
碩士國立雲林科技大學
電子與資訊工程研究所
94
The object detection and recognition is failure on static image of an image recognition system that is caused by the complex background of the scene environment. The characteristic of the dynamic image will not encounter the restrictions of the object and background environment that it interests to the moving object. This thesis utilizes this characteristic to detect dynamic vehicle and gesture. We propose two methods to improve the efficiency of dynamic vehicle and gesture detections and increase the precision rate of the tracking object. First, license plate recognition system consists of image capture, vehicle detection, vehicle tracking, license plate location and character recognition. License Plate recognition system requires more complex computation such that real-time processing is difficult; thus, a FPGA design of License Plate recognition technique with real-time processing is necessary. The simulation results show that the License Plate recognition system has high precision of the license plate segmentation in real-time processing. Second, the gesture tracking and segmentation system is proposed based on dynamic object detection and skin-color model techniques. Our method combines skin-color and hand motion information to detect and segment the gesture in complex background. Experimental results show that the gesture tracking and segmentation technique is able to detect different skin-color in complex background and segment the region of hand for gesture recognition.
17
Chang, Yu-chieh, and 張昱傑. "A Fast License Plate And Vehicle Detection System For Multi-lane." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/45439281941261434912.
Повний текст джерелаАнотація:
碩士大同大學
資訊工程學系(所)
101
Most current license plate detection (LPD) researches target only on the vehicles on single-lane or in short distance to the camera, i.e. each frame contains only one vehicle, which are not suitable for Intelligent Transport Systems, because license plates detection directly on multi-lane would fail for the adopted edge features would be interfered by varying vehicles and background. Here, we proposed a LPD system which can be applied to both single-lane and multi-lane for road monitoring. In this paper, we proposed a system architecture which is based on local binary patterns (LBP) for vehicle detection and edge detection for license plate locating. By adaptive range detection and integral image, real time processing could be achieved for multi-lane license plate detection. In order to locate license plate, hierarchical approach which detect vehicle firstly is more efficiently. In order to verify the feasibility of proposed system, 10 video recoded in Taipei urban area including single-lane and multi-lane under sunny, cloudy, and rainy days are tested. The average vehicle detection rate is 97% and the LPD rate reached 94% and 93% for the single-lane and multi-lane, respectively.
18
Bai, Chu-Ping, and 白楚平. "Real-Time License Plate Detection and Recognition Based on LabVIEW System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/rwp9w6.
Повний текст джерелаАнотація:
碩士國立臺灣海洋大學
機械與機電工程學系
105
In this thesis, LabVIEW system is combined with a webcam to build an Automatic License Plate Recognition System. The image taken by ALPRS is transformed to grey scale image from color space first. Then, the white noise is removed by median filter. Canny edge detection method is used to detect the license plate in the image. Finally, after further morphology process, letters and numbers in the plate image are extracted through template pair technique using pattern matching to achieve license plate recognition.
19
Hsu, Ting-Hsuan, and 許庭瑄. "License plate detection and recognition system based on convolutional neural network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/phvh67.
Повний текст джерелаАнотація:
碩士義守大學
電子工程學系
107
In recent years, automatic license plate recognition (ALPR) system is applied in some traffic-related applications based on deep learning. However, most ALPR systems capture a mostly frontal view of the vehicle and license plate (LP) to obtain high LP recognition rates. This is because the traditional ALPR cannot capture the correct area of oblique LP which results in an error in character recognition or missing characters. As a result, the traditional ALPR will largely reduce the accuracy of recognition for oblique LP. Recently, Silva et al. [12] proposed a warped planar object detection (WPOD) based on convolutional neural network (CNN) to overcome the oblique views of LP. In order to achieve an ALPR system of high accuracy, they divided ALPR into three stages. The first stage is to locate vehicles through YOLOv2 [9]. And then, the second stage locates the oblique LPs and allows a rectification of the LPs area to a rectangle which resembles a frontal view through the WPOD network. Finally, the rectified LPs are fed to an optical character recognition (OCR) in the third stage. Although the WPOD network can achieve the location and rectification of LPs, the loss function of WPOD render the confidence parameter due to high computational complexity. This also leads to WPOD network cannot locate the optimal LP bounding box. In order to further improve the accuracy of ALPR system, we proposed a modified WPOD network using a complete loss function. The proposed method first develop a simple intersection over union (IOU) algorithm to speed up the calculating process of confidence. Therefore, the modified WPOD network can obtain higher LP recognition rate since it considers the confidence parameter in loss function. In this thesis, the four-vertex coordinates of the label bounding box and prediction bounding box of oblique LP are used to generate two rectangular boxes, and then a simple IOU algorithm is used to fast calculate the approximate value of IOU. As a result, a more exact loss function can be finished. In order to compare the performance of LP recognition rate, we train and test the WPOD and the proposed modified WPOD in databases including OpenALPR EU, BR [19], and AOLP RP [17]. Simulation results show that the proposed ALPR system can obtain higher score ratios than those of Silva’s method. And the proposed system can arrive a high accuracy of LP recognition about 95% on an average. In addition, the proposed system also can achieve higher recognition rate about 1% when compared to the Silva’s ALPR system.
20
Wang, Shih-han, and 王詩涵. "Automatic Multiple Vehicles’ License Plate Detection and Recognition System in Complex Environment." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/25731700373072197341.
Повний текст джерелаАнотація:
碩士國立臺灣科技大學
電子工程系
101
This system is specially built for Taiwan’s environment. It has 4 parts: image preprocessing, multiple license-plate detection, normalization and recognition. In image preprocessing, this system takes the benefit of license plate’s color feature then come out a color differential edge detection (CDED) algorithm combines with horizontal Sobel mask, which can erase 62.5% of uninteresting pixels. In detection part, finding edge pixel of character feature then using morphology and density filter extracts the real license plate region. This method can find all license plates region in one scan and has 96.41% succession rate therefore consuming time is 168ms. Normalization part has to solve license plate’s rotation and warp to increase recognition succession rate. This system uses characters in license-plate to estimation rotation angle, and can normalize angle of 0°~36°. In recognition part, projection segmentation combines with segmentation region estimation method can segment sticky characters and increase recognition rate. Finally this system uses probability templates as OCR which has weight for each pixel for every character, and it is fast and has 97.89% succession rate. Of all, this System’s recognition rate is 89.16% and consuming time is 241ms. This paper also collects nowadays LPR related papers, and this system has less restricts and higher succession rate than those.
21
Jiang, Jia-bin, and 江嘉斌. "Wiener-Deconvolution Vertical Edge Enhancement Method for License Plate Detection and Its Android Embedded System Implementation." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/48544259186305328633.
Повний текст джерелаАнотація:
碩士雲林科技大學
電機工程系碩士班
98
Related researches and applications of License Plate Recognition (LPR) have been proceeding for decades. But, in practice, the license plate detection rate is usually error-prone to various backgrounds, illuminations, or skews. So is the subsequent character recognition rate. This is why most of the license plate recognition system just can work well under specific circumstances. Specifically, License plate detection is the fundamental step in LPR. In general, the license plate region is usually detected by the strong vertical edge feature of its interior characters. However, in the external environment, many backgrounds always have strong vertical edges as the license plate. This paper focuses on enlarging the vertical edge differentation between the license plate region and the non-plate region, that is, focuses on enhancing the vertical edge density and strength of the license plate region and weakening those of the non-plate region, e.g. weakening those of the grille. Therefore, the deblurred method of Wiener deconvolution is proposed to enhance and increase the vertical edges of the license plate. Then, 2-level 2D Discrete Wavelet Transform (DWT) is adopted to run the projection histogram of vertical edges. Furthermore, with the first-order local recursive Otsu segmentation, mathematical morphology, and edge density verification method, the license plate region can be detected smoothly. Experimental results show LPD system based on the proposed Wiener-deconvolution vertical edge enhancement method and 2-level 2D DWT, can achieve a much higher hit rate. After extracting the license plate region smoothly, the optical character recognition library of Tesseract OCR can be applied to recognize the license plate characters. On the other hand, this thesis implements a LPR with the Wiener-like vertical edge enhancement method onto Android embedded platform. The implementation result also verifies the Wiener-like vertical edge enhancement method is effective and feasible.
22
Chung, Meng-Liang, and 鐘孟良. "The Study of Vehicle Detection Technologies for the Application of License Plate Recognition Systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/v3nwbn.
Повний текст джерелаАнотація:
博士國立交通大學
電控工程研究所
104
With the development for “Internet of Things (IoT)” technology, Internet of Vehicle technology has become mature. Such systems, nevertheless, focus only on individual vehicle’s driving conditions and often ignore that of the surrounding vehicles, thus making “Internet of Vehicle (IoV)” technology significantly crucial. This paper proposes two systems to obtain the big data analytics of moving vehicles, and to construct a safety and wisdom road system by IoV. The first proposed system utilizes the location of the vehicle to trigger the camera to capture an image of the vehicle. This not only achieves the goal of detecting an image in real time, but as well obtains the best image with the license plate as a triggering image. The proposed algorithm does not require additional hardware, facilitating the precise retrieval of an image that both contains a vehicle and represents the best image from a series of images for recognizing the characters on license plates. This paper proposes another effective real-time detection algorithm that uses two low-cost compact Complementary Metal-Oxide Semiconductor (CMOS) cameras as vision sensors to detect front vehicles and measure distances. This low-cost vision sensor is an asynchronous vision system since a custom-made binocular is used to capture pairs of images via a low-cost grabber card and the left and right images have a slight time difference. Nevertheless, the proposed detection algorithm, which comprises four modules—namely, image preprocessing, vehicle detection, detected vehicle tracking, and distance measurement—is unrestrained by the limitations of the epipolar constraints for synchronous vision systems, and also enables the system to overcome the asynchronous detection problem affecting conventional low-cost vision systems. The results of long-term performance tests conducted on highways and urban and country roads confirm that the proposed system can successfully detect the distance of the front vehicle. The proposed algorithms significantly reduce the cost of hardware and are much easier and cheaper to maintain than the traditional detection methods. An extensive vehicle-detection test demonstrates that the proposed algorithm is reliable and accurate. The analysis of the big data obtained by the proposed algorithms can provide the information of IoV and construct a safety and wisdom road system.
23
tsai, Sung-nien, and 蔡松年. "Dynamic Car License Plate Detection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/22083988530781627442.
Повний текст джерелаАнотація:
碩士中華技術學院
電子工程研究所碩士班
98
License plate recognition system is widely used in a lot of areas such as in the automation of parking lot toll station and in helping to detect stolen vehicle. Previous studies are essentially in static system needed to use a image in which the license plate is fangzheng, however, in this way the vehicle's location and the mobility and the environment must be satisfied some conditions. This study focuses on the dynamic vehicle license plate recognition using video car image when car is moving. In the first, the source images are filmed from the camera. Then, make a series of processes to the images as filtering, edge detection, binarization, rotation…etc. And then, using the binary images locates the preliminary position of the license plate. Finally, a template matching method is to be used to obtain more precise position of the license plate location. The actual recording films of the moving vehicle are used to test. The results of the test verify the effectiveness of the proposed method.
24
Chan, Shan-Lun, and 詹尚倫. "Double-line License Plate Detection and Character Segmentation in License Plate Recognition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/16458793482041748762.
Повний текст джерелаАнотація:
碩士國立交通大學
資訊科學與工程研究所
104
As automatic license plate (LP) localization and recognition getting popular, the requirement of accuracy is rising, too. Most of LP localization systems can be divided into two categories, one is based on technique of image processing and computer vision using edge detection methods such as Sobel operator for gradient computation and search for areas with high texture density as reasonable LP positions; the other is based on machine learning using adaptive boosting with Haar-like features. The accuracy of both methods is close to 100%. Based on the results of localization, character segmentation can be performed by projection and final license plate recognition (LPR) rate up to 95% can be achieved. However, almost all LPR methods are aimed to handle single-line LPs, and may lead to poor recognition rate for the case of double-line LP. To handle such a problem, we propose an image processing method to handle skewed and double-line LP. The proposed approach first performs skew correction of LP and determines whether it has double lines. After line separation, followed by character segmentation performed for each line, single-line LPR can be applied. Experimental results show that the proposed approach is indeed effective in dealing with the situation of skewed LP which may have single or double lines.
25
Chow, Jiunn Nan, and 周俊男. "License Plate Recognition System." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/35737856446114458856.
Повний текст джерелаАнотація:
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%.
26
Hsu, Po-Cheng, and 許伯誠. "License Plate Recognition System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/05825350829541238670.
Повний текст джерелаАнотація:
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.
27
Wang, Chung-Shan, and 王中山. "Using Wavelet for License Plate Detection." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/17481954720232205208.
Повний текст джерелаАнотація:
碩士國立中山大學
機械與機電工程學系研究所
92
Based on digital image processing techniques, the goal of this work is develop a method to automatically detect license plates. To achieve this goal, this thesis uses wavelet transform to first find the position of the license plate. A number of image processing techniques are then developed to identify each character on the license plate. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach, which is the followed by a simple conclusion.
28
Chen, Ching-Hung, and 陳慶鴻. "The Design of License Plate Detection." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/8h7muv.
Повний текст джерелаАнотація:
碩士正修科技大學
電機工程研究所
95
The vehicle management consists of traffic flow control, vehicle crime preventing and the punishment in the violation on vehicle’s rules and regulations. With the improvement of living level in Taiwan, the quantity of vehicle and motorcycle is constantly increasing, and the government has been devoting its effort to plan an efficient transport network, to monitor the traffic at the key crossings and the management of parking lots. However practicable the execution of the traffic scheme runs, it is necessary to apply the license plate recognition on the punishment to the violator who doesn’t obey traffic rules and the automatic charge system for vehicle parking. In this thesis, the study is orientated on the license plate detection under the common climatic environment. Generally speaking, the license plate detection is the primary and first job in the license plate recognition. As the border of the license plate is distinguished from the background, the follow-up character segmentation and character recognition are easily conducted. This study applies edge detection and morphology method to find the possible candidate area with the help of the geometry characteristic of the license plate. There are 318 car images used to test the functionality of the proposed method, which were captured outdoors at different distance, different capture angles and azimuths, and at day and night. The experimental result shows the proposed method can achieve the correct license detection of 95% at best.
29
Chuang, Chia-Lung, and 莊佳龍. "Vehicle Detection and License Plate Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/57361447713712641354.
Повний текст джерелаАнотація:
碩士國立中正大學
光機電整合工程研究所
93
License plate recognition system has been extensively used in a variety of applications, such as parking lot management of community and buildings, and searching stolen car of police departments, roadway monitor, car management and so on。In the past study, the needed car is catched by the method of responding and activating. In this system, the video camera continuously shoot the motion-based vehicle and the video frames are analyzed by shadow detection, vehicle location, size of license plate to catch the needed image including vehicle automatically. Moreover, a car can be recognized more times according to the characteristics of Multiple-frames to increase the probability of recognition, since the system can be used in reality more practically than which using shooting statically.
30
Su, Hao-Ping, and 蘇浩平. "License Plate Detection in the Wild." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/89h2nh.
Повний текст джерелаАнотація:
碩士國立臺灣科技大學
電機工程系
105
License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable conditions, we explore the latest real- time deep learning based detectors, namely YOLO (You- Only-Look-Once) and SSD (Single Shot Multi-Box Detector), and customize them for effectively handling the LPD. The prime modifications include the following: 1) Modifying of fully connected layer on YOLO, 2) Tuning of multi-scale feature maps, anchor boxes, and aspect ratios on SSD, and 3) Creation of a more complete and rigorous AOLPE (Application-Oriented License Plate Extended) database for robust performance evaluation. The AOLPE database is an extended version of the AOLP (Application-Oriented License Plate) database with additional images taken under extreme but realistic conditions. As the original YOLO and SSD are not intended for LPD and they failed miserably as LPDs, the performances of the proposed customized versions of both YOLO and SSD are directly evaluated on the AOLPE database. The contributions made in this study is not only a pioneering customized exploration of state-of- the-art real- time deep learning approaches for handling in-the-wild LPD, but also involves the release of the AOLPE database and evaluation protocol to define a novel and practical benchmark for LPD.
31
Chiang, Chang-Yun, and 江長運. "Plate- and Character-based AdaBoost License Plate Detection and Its Android License Plate Recognition Recorder Implementation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/86923239518107813208.
Повний текст джерелаАнотація:
碩士國立雲林科技大學
電機工程系碩士班
100
License Plate Detection (LPD) is the fundamental step to License Plate Recognition (LPR). If the license plate localization result of LPD is not exact enough, subsequent OCR step is unnecessary to be carried out. Most especially, on mobile or portable LPR devices, the accuracy of LPD severely suffers from the environmental interferences, like complex background, illumination variation, scene alternation. So most traditional LPR system only can work out for invariant and specific scenes. In order to overcome the LPD difficulty of mobile or portable LPR devices, this paper proposes plate- and character-based AdaBoost LPD with vertical-edge-based auxiliary LPD. The proposed plate-based AdaBoost LPD is to localize 0 or more license plate candidates firstly, and then, the proposed character-based AdaBoost LPD is to verify and filter out the most accurate license plate candidate. As for the vertical-edge-based auxiliary LPD, it is activated to double check the scene only after no license plate candidate is detected by the proposed plate- and character-based AdaBoost LPD. From experimental results, it is found that the proposed LPD method can achieve excellent and stable accuracy under various conditions of background complexity, illumination, and scene. Finally, this paper implements the proposed LPD method and license plate character recognition module into a vehicle video recorder featuring real-time and continuous LPR. The LPD rate and LPR rate of the implementation are 99% and 98%, respectively.
32
Tsai, Chien-Tsai, and 蔡建材. "Automatic License Plate Recognition system." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/48475416792944540060.
Повний текст джерелаАнотація:
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.
33
Wang, Hui-Chuan, and 王惠娟. "License Plate Automatic Identification System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/71539842292443773183.
Повний текст джерелаАнотація:
碩士遠東科技大學
機械工程研究所
99
License plate recognition can be divided into the following three sub-systems: license plate location, character segmentation and character recognition. In this study the main stress falls on the license plate location and character recognition sub-systems. The images of vehicles in the parking lot on campus would be the test examples to examine the license plate recognition system developed by our research. The license plate recognition system would find the location of license plate and recognize the characters on it. The main difference between then general building and campus parking lot is then entrance control policy. In most case, there are cameras on the gateway of general building to record the images of residents’ or visitors’ vehicles. The license plate images captured by this system have large pixels relatively. The length of the license plate image is almost more than 100 pixels.In this research, we take 238 images of vehicles on campus parking lot. In these images, the length of license plate image is about 60 ~ 130 pixels. For the part of license plate location, the most of the studies deal with the edge detection process. In this study, the standard deviation process would be proposed to find corner features of license plates. The results show that detection rate of the license plate location up to 96%. In character recognition, we propose a classified method based on the structure of character. We divide the structure of character into nine regions and each region has a code parameter. These code parameters would be used to character recognition. In preliminary stage, the recognition rate is very low (1%). However, we propose three enforcement methods, such as limited character structure, single-side shift, double-side shit, to improve the recognition accuracy. Finally, character recognition accuracy could be up to 97%.
34
Tseng, Chien-hao, and 曾建豪. "Smartphone License Plate Recognition System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/36788628071050399131.
Повний текст джерелаАнотація:
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.
35
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.
Повний текст джерелаАнотація:
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.
36
Chang, Chih-Chieh, and 張智傑. "Vehicle License Plate Detection Using Integral Image." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/k3n682.
Повний текст джерелаАнотація:
碩士大同大學
資訊工程學系(所)
102
Locating and recognition of vehicle license plate has always been a research direction of image processing. Compared to traditional methods using color and edge features for license plate detection, the proposed block scanning of edge image could immediately identify candidate plate regions of different distances. By using edge informant, the proposed is also effective and robust under variety of weather conditions. Firstly, the proportion of license plate is used to setup possible block sizes for edge image scanning. While doing block scanning, we could quickly filter out false positives by characteristics of license plates such as upper/lower blank area, as well as fixed font of license plate characters. Furthermore, license plate character could be adjusted and recognized by vertical projection. The used filtering criteria not only can reduce the complexity of image operations but also robust under varying weather conditions for license plate locating and recognition. In order to verify the feasibility of the system, we tested 155 images in daytime/evening/night and sunny/rainy day and the average license plate detection rate was 95%. In the future, the effectiveness of the overall system performance could be improved by developing more criteria for traffic control or parking access control.
37
Wu, Meng-Tsung, and 吳孟璁. "Automatic Vehicle License Plate Recognition System." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/96706197081543255915.
Повний текст джерелаАнотація:
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.
38
Huang, Yu-jung, and 黃鈺榮. "DSP-Based License Plate Rcognition System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/56264245796169080783.
Повний текст джерелаАнотація:
碩士國立聯合大學
電機工程學系碩士班
98
A fast license-plate recognition algorithm will be proposed in this thesis. It has two main features as described in the following: (A) The so-called Color Repetition Density (CRD) is introduced to be a measure of license plate location. (B) The scan coding technique is developed to achieve license plate OCR with very low computation complexity. This algorithm is verified by the simulation using MATLAB. The corresponding results show the rate of success is 86%. In addition, it is further ported to TI’s DSP chip named DM-6437 to test its real performance of license plate detection. The result is about 5 fps at full 720x486 frame size. We believe our proposed algorithm will be very practical and useful in the field of license-plate recognition.
39
OU, YUAN-LIANG, and 歐原良. "A Portable License Plate Recognition System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37920587265579440474.
Повний текст джерелаАнотація:
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.
40
CHEN, YI-AN, and 陳怡安. "License-Plate Recognization System for School." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/48277871401523615776.
Повний текст джерелаАнотація:
碩士僑光科技大學
資訊科技研究所
102
License plate recognition(LPR) technology has been developed over the years, and this technology has been used in the parking lot, but mostly fixed formula based, due to the volume of a small locomotive and moving fast, is not suitable for use sentinel-like way, so in most cases, the human way vehicles for parking violations billing action. This study aims to identify your smartphone confirm whether the vehicle license plate number and have to pay the parking fee, in order to enhance the efficiency of management. In order to implement the convenience and practicality, this study locked vehicle license plate recognition research campus in. The system uses the Windows Server operating system with Visual Basic erection Application Server, use SQL Server as the database record data on, and license plate recognition using Tesseract-OCR is to identify the license plate number. The results of this study can be carried out quickly LPR through intelligent communication devices, networks and services, and through the mechanism of the rapid verification server, and can be easily and efficiently learn information vehicles. Three experiments designed compared with other studies, the final results of this study that the campus license plate recognition system can be the most efficient query with high security vehicle information, and superior to other systems.
41
Song, Yong-Sheng, and 宋永勝. "License Plate Recognition and Management System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/aqrwhy.
Повний текст джерелаАнотація:
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.
42
Lu, Chien-Te, and 呂建德. "Implementation of License Plate Recognition System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/ne2t59.
Повний текст джерелаАнотація:
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%.
43
Chu, Yu-Kai, and 朱昱暟. "Preliminary Study for the Detection of License Plate." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/26157255937510924805.
Повний текст джерелаАнотація:
碩士中華技術學院
電子工程研究所碩士班
97
The purpose of this study is exacting the location of car license plate for the car license recognition system to obtain the contents of the car license. In the beginning process, a median filter and a vertical edge enhanced filter are used to reduce the disturbance from background and to reveal the characteristics of car license. The next step is detecting the pattern edge by using a gray-scale gradient operator which can make the edge of car license clearer. Then, a binarization process for reducing the amount of data to speed up the process for the next process is used. After binarization processing, the white spots on the car license are very intensive and obvious. Therefore, one can calculate the number of white spots in the fixed wide and high ratio rectangle zone and scan over the whole image to find out the maximum value to obtain the rough location of the car license plate in the image. After the rough car license plate location zone is obtained, an algorithm is used to rotate the rough zone of the license plate to the right angle. And then, the pixels in the image which do not belong to car license plate are eliminated by using RGB color separation. In the final step, some established car license plate templates are used to match with the rough zone obtained in previous procedures to accurately locate the car license plate location. Experimental results show that the proposed algorithm can work well.
44
Chen, Ko-Chih, and 陳克智. "License Plate Detection and Recognition of Smart-Phone." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/01428773053617602065.
Повний текст джерелаАнотація:
碩士國立中央大學
資訊工程學系碩士在職專班
99
This paper presents an approach for license plate recognition using a camera-equipped smartphone. The proposed method provides a reliable and accurate technique to solve the problem of license plate recognition caused by the skew and shadow on the license plates. There are four stages in the proposed approach: license plate location, license plate rectification, character segmentation and character recognition. In the first stage, we locate the license plate by accumulating edge points, and then analyze the edge points and accumulation associated with vertical and horizontal dimensions of the image. As to the second stage, license plate rectification, we adopt local threshold to cope with the problem of shadow on the plates first. Next step involved analyzing black and white pixels in order to decide whether to invert the image or not. The researcher tries to engage the characteristics like length-width ratio, size, and position of the bounding box in the text region to eliminate the non-text portions. To solve the rotation, skew, and scale problems of the slanted license plates in the image, we use an affine transformation to estimate the skew angle. Edge points vertical direction accumulating and trough are used to segment characters section in the third stage. We normalize the characters size to 40 × 90. Finally, criterion of normalized cross-correlation is used in the last stage for character recognition. In behalf of shortening the process time for identification, the procedure of character reorganization is improved. We shrink the samples to one-fourth the size to conduct the first identification process. Then, three highest-coefficient samples are chosen to match the original input pattern. From these three samples, the highest-coefficient one is selected as the final result.
45
Gajula, Nanda kishore. "Study of object detection and reading(license plate detection and reading)." Thesis, 2011. http://ethesis.nitrkl.ac.in/2857/1/nanda_final.pdf.
Повний текст джерелаАнотація:
Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized.
Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques.
In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique.
Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other.
46
Chhabada, Sandeep Singh. "Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system." Thesis, 2012. http://ethesis.nitrkl.ac.in/3352/4/Heuristics_for_license_plate_localization_and_hardware_implementation_of_Automatic_License_Plate_Recognition_(ALPR)_system.pdf.
Повний текст джерелаАнотація:
The project “Heuristics for license plate localization and hardware implementation of Automatic License Plate Recognition (ALPR) system” deals with detection and recognition of license plate from a captured front view of any car. The work follows all the steps in an ALPR system like preprocessing, segmentation, and license plate identification, extraction of individual characters and finally recognition of each character to form a string to match with the registered License plate numbers. The main contribution in the work is to expedite the number plate isolation from a set of segmented candidates. It utilizes a set of heuristics typically transition from object to background and vice-versa, aspect ratio of the bounding boxes. This narrow down the number of candidates for further processing and further, we suggest a rank based identification of each character in the number plate. The process scheme along with the existing methodologies is integrated to develop the overall ALPR system. A set of standard images collected from internet as well as self-collected car images of staff vehicles are used for simulation. The experiments are conducted using OpenCV. For validation, a working ALPR hardware prototype is developed using AVR development board (ATmega32 microcontroller), GP2D120 distance measurement sensor (IR-sensor).Interfacing between PC and controller-board is done using serial port. The model works with an accuracy of 80%. The ALPR system has a further scope to improve the recognition speed using parallel processing of various sub-steps.
47
Guo, Ming-Feng, and 郭銘峰. "An Efficiency Recognition System for License Plate." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/70870433128232365966.
Повний текст джерелаАнотація:
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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Chen, Hsiang-Chieh, and 陳翔傑. "Automatic vehicle license plate recognition system design." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26720851603323695840.
Повний текст джерелаАнотація:
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.
49
Chen, I.-Chih, and 陳奕志. "Constructing Embedded Car License Plate Recognition System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/91717196909075534200.
Повний текст джерелаАнотація:
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.
50
CHIOU, CHIH-KUO, and 邱智國. "Implementation of A License Plate Recognition System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/dkfvjb.
Повний текст джерелаАнотація:
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.