Relevant bibliographies by topics / Automatic Number Plate Recognition (ANPR)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Automatic Number Plate Recognition (ANPR)'

Author: Grafiati
Published: 11 December 2022
Last updated: 25 January 2023

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Journal articles on the topic "Automatic Number Plate Recognition (ANPR)"

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Lubna, Naveed Mufti, and Syed Afaq Ali Shah. "Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms." Sensors 21, no. 9 (April 26, 2021): 3028. http://dx.doi.org/10.3390/s21093028.

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.
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Palampatla, Hrithik Roshan. "Automatic Number Plate Recognition Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 2394–400. http://dx.doi.org/10.22214/ijraset.2021.36889.

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Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.
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Gunawan, Teddy Surya, Abdul Mutholib, and Mira Kartiwi. "Performance Evaluation of Automatic Number Plate Recognition on Android Smartphone Platform." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 1973. http://dx.doi.org/10.11591/ijece.v7i4.pp1973-1982.

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<span>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.</span>
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Gunawan, Teddy Surya, Abdul Mutholib, and Mira Kartiwi. "Design of Automatic Number Plate Recognition on Android Smartphone Platform." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 99. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp99-108.

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<p>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. It is a combination of hardware and software designed to offer the optimum reliability. Since the past decades, many researchers have been proposed to recognize the vehicle number plate and implemented it in various access control, law enforcement and security, including parking management system, toll gate access, border access, tracking of stolen vehicles and traffic violations (speed trap, illegal parking, etc). However, previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design of ANPR in Android smartphone platform which has limited camera resolution and limited computational power. The main challenges of implementation ANPR algorithm on smartphone are higher coding efficiency, lower computational complexity, and higher the scalability. The objectives of this research is to design ANPR on Android smartphone, including graphical user interface (GUI) design, process design, and database design. First, a comprehensive survey on the pre-processing, segmentation, and optical character recognition is conducted. Secondly, proposed system development and algorithm implementation is explained in more details. Results show that our proposed design can be implemented effectively in Android smartphone platform.</p>
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Patel, Chirag, Dipti Shah, and Atul Patel. "Automatic Number Plate Recognition System (ANPR): A Survey." International Journal of Computer Applications 69, no. 9 (May 17, 2013): 21–33. http://dx.doi.org/10.5120/11871-7665.

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Sugeng, Sugeng, and Eniman Yunus Syamsuddin. "Designing Automatic Number Plate Recognition (ANPR) Systems Based on K-NN Machine Learning on the Raspberry Pi Embedded System." JTEV (Jurnal Teknik Elektro dan Vokasional) 5, no. 1.1 (September 23, 2019): 19. http://dx.doi.org/10.24036/jtev.v5i1.1.106135.

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Research on vehicle number plate recognition or Automatic Number Plate Recognition (ANPR) is mostly done by researchers to produce an introduction that has high accuracy. Several methods of introduction are carried out such as introduction to edge detection and morphology, relationship analysis between objects, machine learning and deep learning. In this research a K-NN machine learning ANPR system was developed in character recognition. The method of analyzing relationships between objects is used to localize number plates. The system that was developed also added an artificial intelligence to be able to find out the fault of the number plate recognition and fix it based on the position of the character group in the number plate. The ANPR system is designed to be an Embedded system so that it can be implemented to be able to carry out the identification of two-wheeled and four-wheeled vehicle license plates. The ANPR system was also developed to be used in the parking management system. In this research the recognized number plates are limited to private number plates in Indonesia. In testing, the system is made capable of recognizing the number plates of two-wheeled vehicles and four-wheeled vehicles on vehicles that have a standard license plate according to Polri regulations, both in the font type and the number plate writing format. The results of vehicle number plate recognition reached an accuracy of 98%.
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Rehman, Saif Ur, Moiz Ahmad, Asif Nawaz, and Tariq Ali. "An Efficient Approach for Vehicle Number Plate Recognition in Pakistan." Open Artificial Intelligence Journal 06, no. 1 (May 9, 2020): 12–21. http://dx.doi.org/10.2174/1874061802006010012.

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Introduction: Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing. Aim: ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. Different ANPR systems have been suggested for different countries, including Iran, Malaysia, and France. However, only a limited work exists for Pakistan vehicles. Therefore, in this study, we aim to propose a novel ANPR framework for Pakistan VLNP recognition. Methods: The proposed ANPR system functions in three different steps: (i) - Number Plate Localization (NPL); (ii)- Character Segmentation (CS); and (iii)- Optical Character Recognition (OCR), involving template-matching mechanism. The proposed ANPR approach scans the number plate and instantly checks against database records of vehicles of interest. It can further extract the real=time information of driver and vehicle, for instance, license of the driver and token taxes of vehicles are paid or not, etc. Results: Finally, the proposed ANPR system has been evaluated on several real-time images from various formats of number plates practiced in Pakistan territory. In addition to this, the proposed ANPR system has been compared with the existing ANPR systems proposed specifically for Pakistani licensed number plates. Conclusion: The proposed ANPR Model has both time and money-saving profit for law enforcement agencies and private organizations for improving homeland security. There is a need to expand the types of vehicles that can be detected: trucks, buses, scooters, bikes. This technology can be further improved to detect the crashed vehicle’s number plate in an accident and alert the closest hospital and police station about the accident, thus saving lives.
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Singh, Sneh Kanwar, Dr Raman Maini, and Dr Dhavlessh Ratan. "Automatic Number Plate Recognition System using Connected Component Analysis and Convolutional Neural Network." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 167–73. http://dx.doi.org/10.35940/ijeat.f1636.1011121.

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Technology is becoming constantly important for customers. Automatic number plate Recognition (ANPR) is a device which enables the identification of a number plate in real time. For an intelligent car service, ANPR helps to promote growth, customize the classic app and increase consumer and employee productivity. Within the specification, the principal function of ANPR lies of removing the characteristics from an illustration of a license plate. An application that enables customers to display automobile repairs through the license platform number only derived from a loaded picture is augmented by a smart car service. Technological progress is that, so it is thought that improvement is important in this region too, so the best choice for automotive services is a smart car company. This work proposed a methodology to detect the numbers from car license plate using convolutional neural network. In the preprocessing of photographs on license plates, the WLS and FFT filters were included. The images are then fed into the convolutional trainings neural network. On more plates and tests is reported during the testing. Therefore, the findings indicate that the proposed solution can be taken in less time from the license model to accurately identify the characters. The experimental result shows the significance of proposed research by achieving an accuracy of 98% for the localization and true recognition of license plates from the video frames.
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A. G, S. Fakhar, M. Saad H, A. Fauzan K, R. Affendi H., and M. Aidil A. "Development of portable automatic number plate recognition (ANPR) system on Raspberry Pi." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1805. http://dx.doi.org/10.11591/ijece.v9i3.pp1805-1813.

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ANPR system is used in automating access control and security such as identifying stolen cars in real time by installing it to police patrol cars, and detecting vehicles that are overspeeding on highways. However, this technology is still relatively expensive; in November 2014, the Royal Malaysian Police (PDRM) purchased and installed 20 units of ANPR systems in their patrol vehicles costing nearly RM 30 million. In this paper a cheaper alternative of a portable ANPR system running on a Raspberry Pi with OpenCV library is presented. Once the camera captures an image, image desaturation, filtering, segmentation and character recognition is all done on the Raspberry Pi before the extracted number plate is displayed on the LCD and saved to a database. The main challenges in a portable application include crucial need of an efficient code and reduced computational complexity while offering improved flexibility. The performance time is also presented, where the whole process is run with a noticeable 3 seconds delay in getting the final output.
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Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan, Adil Zulfiqar, and Muhammad Tahir Bhatti. "Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR." Complexity 2021 (October 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/5597337.

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The metropolis of the future demands an efficient Automatic Number Plate Recognition (ANPR) system. Since every region has a distinct number plate format and style, an unconstrained ANPR system is still not available. There is not much work done on Pakistani number plates because of the unavailability of the data and heterogeneous plate formations. Addressing this issue, we have collected a Pakistani vehicle dataset having various plate configurations and developed a novel ANPR framework using the dataset. The proposed framework localizes the number plate region using the YOLO (You Only Look Once) object detection model, applies robust preprocessing techniques on the extracted plate region, and finally recognizes the plate label using OCR (optical character recognition) Tesseract. The obtained mAP score of the YOLOv3 is 94.3% and the YOLOv4 model is 99.5% on the 0.50 threshold, whereas the average accuracy score of our framework is found to be 73%. For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. The comparative analysis shows that the proposed ANPR framework comprising the YOLOv4 and OCR Tesseract has good accuracy and inference time for a wide variation of illumination and style of Pakistani number plates and can be used to develop a real-time system. The proposed ANPR framework will be helpful for researchers developing ANPR for countries having similar challenging vehicle number plate formats and styles.
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Dissertations / Theses on the topic "Automatic Number Plate Recognition (ANPR)"

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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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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
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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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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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Molčány, Peter. "Zpracování obrazu v systému Android - detekce a rozpoznání SPZ/RZ a využití externí databáze zájmových vozidel." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221270.

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The aim of this Master's thesis is designing and developing Android application for automatic number plate recognition with external database lookup. In the introduction we discuss possibilities of number plate recognition in general. Android platform fundamentals, necessary developer tools and multi-platform image processing library OpenCV are described in the second section. In the third section different database types and synchronization methods are introduced. In the fourth section we describe basics of image processing and different algorithms necessary for recognition. Application requirements, involving scene and hardware requirements are defined in the fifth section. In the sixth section application development and algorithm implementation is described. In the seventh section we evalute the results of the detection. In the last section results are summarized and goals are set for further improvement.
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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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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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Books on the topic "Automatic Number Plate Recognition (ANPR)"

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Henderson, Charlie. Driving Crime Down,Denying Criminals the Use of the Road, [Automatic Number Plate Recognition (Anpr)]. Stationery Office, The, 2004.

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Book chapters on the topic "Automatic Number Plate Recognition (ANPR)"

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Jadhav, Atharva V., Omkar K. Dongre, Tanmay K. Shinde, Deepak S. Patil, and Jaya H. Dewan. "A Study on Approaches for Automatic Number Plate Recognition (ANPR) Systems." In Advances in Data and Information Sciences, 647–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5689-7_57.

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Pandey, Ayushi, and Rati Goel. "Automatic Number Plate Detection and Recognition." In International Conference on Innovative Computing and Communications, 367–79. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2535-1_29.

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Brunner, David, and Fabian Schmid. "Synthetic Data in Automatic Number Plate Recognition." In Communications in Computer and Information Science, 112–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14343-4_11.

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Al Awaimri, Mohammed, Sallam Fageeri, Aiman Moyaid, Christopher Thron, and Abdullah ALhasanat. "Automatic Number Plate Recognition System for Oman." In Artificial Intelligence for Data Science in Theory and Practice, 155–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92245-0_8.

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Rajasekar, B., B. M. S. Krishna Roshan, B. Chandrababu Naidu, and V. Vijaya Kumar. "Automatic Number Plate Recognition Using Convolution Neural Network." In Sixth International Conference on Intelligent Computing and Applications, 381–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1335-7_34.

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Vaishnav, Arun, and Manju Mandot. "Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition." In Information and Communication Technology for Sustainable Development, 683–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7166-0_69.

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Suneetha, K., and K. Mounika Raj. "Automatic Vehicle Number Plate Recognition System (AVNPR) Using OpenCV Python." In Lecture Notes in Networks and Systems, 487–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1941-0_49.

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Gireesha, H. M., Prabha C. Nissimgoudar, and Nalini C. Iyer. "Fusion of Face Recognition and Number Plate Detection for Automatic Gate Opening System." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 919–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0882-7_83.

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Nirmala, J. S., Rahul Banerjee, and Rajath S. Bharadwaj. "Automatic Vehicular Number Plate Recognition (VNPR) for Identification of Vehicle Using OCR and Tesseract." In Micro-Electronics and Telecommunication Engineering, 403–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_41.

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Filipiak, Patryk, Bartlomiej Golenko, and Cezary Dolega. "NSGA-II Based Auto-Calibration of Automatic Number Plate Recognition Camera for Vehicle Speed Measurement." In Applications of Evolutionary Computation, 803–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31204-0_51.

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Conference papers on the topic "Automatic Number Plate Recognition (ANPR)"

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Kulkarni, Prathamesh, Ashish Khatri, Prateek Banga, and Kushal Shah. "Automatic Number Plate Recognition (ANPR) system for Indian conditions." In 2009 19th International Conference Radioelektronika (RADIOELEKTRONIKA). IEEE, 2009. http://dx.doi.org/10.1109/radioelek.2009.5158763.

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Rhead, Mke, Robert Gurney, Soodamani Ramalingam, and Neil Cohen. "Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems." In 2012 IEEE International Carnahan Conference on Security Technology (ICCST). IEEE, 2012. http://dx.doi.org/10.1109/ccst.2012.6393574.

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Amruta, Kale, Kale Devayani, R. Awale, and Jadhao Bhimrao. "Skew correction process in automatic number plate recognition (ANPR) system." In THE 2ND UNIVERSITAS LAMPUNG INTERNATIONAL CONFERENCE ON SCIENCE, TECHNOLOGY, AND ENVIRONMENT (ULICoSTE) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0107053.

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Ramalingam, S., M. Rhead, and R. Gurney. "Impact of character spacing on the performance of Automatic Number Plate Recognition (ANPR) systems through simulation." In 2014 International Carnahan Conference on Security Technology (ICCST). IEEE, 2014. http://dx.doi.org/10.1109/ccst.2014.6987038.

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Yaseen, Naaman Omar, Salim Ganim Saeed Al-Ali, and Abdulkadir Sengur. "Development of New Anpr Dataset for Automatic Number Plate Detection and Recognition in North of Iraq." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965512.

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Daniel, Debby Ratna, Ivana Laksmono, and Abetia Fitriani. "E-Traffic Operational Information System Based on Automatic Number Plate Recognition (ANPR) System as a Tool to Detect Traffic Violation and to Manage the Traffic Fines in Indonesia." In Journal of Contemporary Accounting and Economics Symposium 2018 on Special Session for Indonesian Study. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0007019206380644.

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Morales Sánchez, Francisco José, Luis Miguel Romero Perez, Noelia Cáceres Sánchez, Antonio Reyes Gutiérrez, and Francisco García Benítez. "Obtención y análisis de aforos desagregados de transportes de mercancías peligrosas mediante sistemas ANPR." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3796.

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La fuente de información disponible, a nivel nacional, sobre flujos de tráfico que involucran mercancías peligrosas se encuentra publicada anualmente en el Mapa de Tráfico editado por el Ministerio de Fomento. Esta información, de carácter informativo, es presentada de forma agregada; obtenida mediante aforos manuales que se capturan en 6 campañas anuales, en días laborables de meses alternos y en un periodo temporal limitado a seis horas. Los datos no revelan la distribución de la tipología de mercancía peligrosa transportada. Esta distribución sí puede inferirse haciendo uso de sistemas ANPR (Automatic Number Plate Recognition), mediante la captura de datos durante las 24 horas diarias, para los 365 días del año. El sistema y metodología que se reporta en este estudio demuestra la capacidad de estimación del flujo de vehículos y tipología de materia peligrosa transportada mediante la lectura del código ONU que identifica de forma unívoca la tipología de ésta y que se encuentra incluido dentro de la placa naranja de identificación de peligro que obligatoriamente deben llevar los vehículos que realizan el transporte de este tipo de mercancía. El sistema se encarga de la interpretación de la materia transportada y el peligro asociado, así como de la posición y dirección de dicho vehículo, registrando la información en un servidor central. El sistema está constituido por un conjunto distribuido de estaciones de lectura ubicadas en puntos de observación estratégicos. Con la información capturada con este sistema automático, se realiza una comparativa con la fuente de información agregada, para derivar conclusiones sobre el grado de utilidad de la información publicada. Con la inferencia de la información desagregada es posible realizar una multiplicidad de análisis tales como, clasificación de materias por su importancia en cuanto al volumen transportado, identificación de los puntos con un mayor riesgo medioambiental, y actualización de matrices de origen-destino de mercancías peligrosas, entre otros.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3796
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De las Heras Molina, Javier, Juan Gómez Sánchez, and José Manuel Vassallo Magro. "Electronic Toll Collection Systems and their Interoperability: The State of Art." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3186.

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The European Electronic Toll Service (EETS) was created in 2004 with the aim of ensuring interoperability among the existing electronic toll collection (ETC) systems in Europe. However, the lack of cooperation between groups of stakeholders has not made possible to achieve this goal ten years later. The purpose of this research is to determine the better way to achieve interoperability among the different ETC systems in Europe. Our study develops a review of the six main ETC systems available worldwide: Automatic Number Plate Recognition (ANPR), Dedicated Short-Range Communications (DSRC), Radio Frequency Identification (RFID), Satellite systems (GNSS), Tachograph, and Mobile communications tolling systems. The research also provides some insight on different emerging technologies. By focusing on different operational and strategic aspects offered by each technology, we identify their main strengths, weaknesses, opportunities and threats and makes different recommendations to improve the current framework. The research concludes that given the diversity of advantages and inconveniences offered by each system, the selection of a certain ETC technology should also take into account its potential to overcome the weaknesses in the current ETC framework. In this line, different policy recommendations are proposed to improve the present ETC strategy at the EU.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3186
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Kashyap, Abhishek, B. Suresh, Anukul Patil, Saksham Sharma, and Ankit Jaiswal. "Automatic Number Plate Recognition." In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2018. http://dx.doi.org/10.1109/icacccn.2018.8748287.

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Sasi, Anumol, Swapnil Sharma, and Alice N. Cheeran. "Automatic car number plate recognition." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8275893.

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