Relevant bibliographies by topics / Car license plate detection and recognition

Academic literature on the topic 'Car license plate detection and recognition'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Car license plate detection and recognition"

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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.
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Mathew, Jess, and Chang Lee. "Vertical Edge Detection for Car License Plate Recognition." DJ Journal of Advances in Electronics and Communication Engineering 1, no. 1 (August 9, 2015): 8–15. http://dx.doi.org/10.18831/djece.org/2015011002.

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Alaidi, Abdul Hadi M., Saif Ali Abd Alradha Alsaidi, and Omar Hashim Yahya. "Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm." Journal of Education College Wasit University 1, no. 26 (January 12, 2017): 449–60. http://dx.doi.org/10.31185/eduj.vol1.iss26.102.

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This paper presents an automated car license plate recognition system applied for Iraqi vehicle plate number that developed and applied to be used in control and law enforcement related applications. In this work, the proposed license plate recognition consists of three basic stages (preprocessing, license plate localization, license plate recognition). The license plate images are pre-processed through convert image to grayscale and apply morphological transformation filter not convert the result to binary image. Then, blurs the binary image using Gaussian filter and find all contour in image using OpenCV library. In the license plate localization KNN (k-Nearest Neighbors) algorithm are used to find all possible characters in the image. The last step is done by Crop the part of image with highest candidate license plate and apply the preprocessing and license plate localization again to find and recognize all part of license plate in the cropped image.
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Hashmi, Saquib Nadeem, Kaushtubh Kumar, Siddhant Khandelwal, Dravit Lochan, and Sangeeta Mittal. "Real Time License Plate Recognition from Video Streams using Deep Learning." International Journal of Information Retrieval Research 9, no. 1 (January 2019): 65–87. http://dx.doi.org/10.4018/ijirr.2019010105.

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With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.
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Farag, Mohamed Sayed, Mostafa Mohamed Mohie El Din, and Hassan Ahmed Elshenbary. "Parking entrance control using license plate detection and recognition." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 476. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp476-483.

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<p>There is no doubt that car parking is a very challenging and interesting topic of surveillance. In the recent years, a lot of smart systems for parking lot access control were developed to control and register the car data. The aim of this paper is to use image processing methods to control the entrance of a smart parking. The steps of car plate recognition are: preprocessing, License plate detection, character extraction and recognition. In the step of preprocessing, image was enhanced and noise was reduced. After preprocessing stage, color filter was used to detect the plate region. In case of large image size DWT was used for feature extraction and decreased the time of the detection stage. In the stage of character segmentation, the image is converted from grayscale to binary according to a given threshold. Filtering the binary image after using the morphological operation method, the largest objects are determined as the segmented plate characters. Finally, the correlation method was used to recognize the segmented characters. In case of similarity, SVM was used as a good classifier. Experimental results using matlab software, view that the proposed method increase the plate detection and recognition rates. It achieved aver- age 97.8% detection rate, 98% segmentation rate and 97% recognition rate, So it will be a good method for smart parking entrance control.</p>
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Aldabbagh, Ali H. A., Laith A. H. Al-Shimaysawee, and Hussein M. H. Al-Rikabi. "Novel Algorithm for Iraqi Car License Plate Detection and Recognition." Journal of Engineering and Applied Sciences 14, no. 1 (December 10, 2019): 205–10. http://dx.doi.org/10.36478/jeasci.2019.205.210.

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Aggarwal, Akarsh, Anuj Rani, and Manoj Kumar. "A robust method to authenticate car license plates using segmentation and ROI based approach." Smart and Sustainable Built Environment 9, no. 4 (October 4, 2019): 737–47. http://dx.doi.org/10.1108/sasbe-07-2019-0083.

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Purpose The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users. Design/methodology/approach From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate. Findings The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent. Research limitations/implications The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city. Originality/value This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.
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Svatiuk, Danylo, Oksana Svatiuk, and Oleksandr Belei. "APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM." Cybersecurity: Education, Science, Technique 4, no. 8 (2020): 97–112. http://dx.doi.org/10.28925/2663-4023.2020.8.97112.

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The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.
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Shevchenko, V., V. Bredikhin, T. Senchuk, and V. Verbytska. "COMPARISON OF METHODS FOR AUTOMATIC LICENSE NUMBER RECOGNITION." Municipal economy of cities 4, no. 171 (October 17, 2022): 7–11. http://dx.doi.org/10.33042/2522-1809-2022-4-171-7-11.

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The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.
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Manoj Prakash, P., Sreerag Premanathan, ShivamKumar Surwase, M. S. Antony Vigil, and Shivam Bohare. "License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3246–51. http://dx.doi.org/10.1166/jctn.2019.8171.

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Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.
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Dissertations / Theses on the topic "Car license plate detection and recognition"

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Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.

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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.

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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.

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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.
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Krajíček, Pavel. "Rozpoznání SPZ/RZ." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218307.

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The theme of this thesi’s deals with the detection and recognition of car license plate from pictures made of screening machine situated on a crassing or inside a car. The thesis si divided into two basic parts. First deals with searching for presence of licence plate in the picture. If the marque was found, we continue the second part of the program which identificates the found license plate. The first part of program aspires to find the licence plate by the edge detectors. The second part classifies characters by the method based on an analytical description.
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D'amore, Luiz Angelo. "Robustez na segmentação de placas veiculares em condições complexas de aquisição." Universidade Presbiteriana Mackenzie, 2010. http://tede.mackenzie.br/jspui/handle/tede/1389.

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Made available in DSpace on 2016-03-15T19:37:29Z (GMT). No. of bitstreams: 1 Luiz Angelo D Amore.pdf: 3689058 bytes, checksum: 8476274d8f5220a2a7978da28a4a4f3d (MD5) Previous issue date: 2010-08-13
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
The work presented here shows a robust method for license plate detection. The term robust in this work is directly related to the efficacy of the system as an automated locator of license plates without human intervention and considering specific characteristics of image acquisition and license plate features. The proposed method is based on the specify features of the digits found on the Brazilian license plates. Although the method was designed for the Brazilian license plate pattern it can be easily adjusted to other patterns. The results obtained using the proposed method showed a better performance than that of other academic approaches and even of commercial systems.
Os sistemas automáticos de reconhecimento de placas veiculares têm como principal função a identificação de veículos a partir de imagens digitais, com aplicações nas áreas de segurança pública e privada. Neste trabalho são apresentadas técnicas de processamento de imagens com o objetivo de desenvolver um método robusto para a segmentação de placas veiculares em condições complexas de aquisição. O termo robusto neste trabalho é relacionado diretamente à eficácia do sistema quanto à localização automática das placas veiculares sem intervenção humana, considerando características específicas das imagens e placas. O método proposto é baseado nas especificidades dos dígitos localizados nas placas brasileiras. Embora o método tenha sido projetado para o padrão de placas brasileiro, pode ser facilmente ajustado para outros padrões. Os resultados obtidos com o método proposto mostram um desempenho melhor que outras abordagens acadêmicas, ou mesmo de sistemas comerciais.
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Vladimir, Tadić. "Fazifikacija Gaborovog filtra i njena primena u detekciji registarskih tablica." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107171&source=NDLTD&language=en.

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Disertacija prikazuje novi algoritam za detekciju i izdvajanje registarskih tablica iz slike vozila koristeći fazi 2D Gaborov filtar. Parametri filtra: orijentacija i talasna dužina su fazifikovani u cilju optimizacije odziva Gaborovog filtra i postizanja dodatne selektivnosti filtra. Prethodno navedeni parametri dominiraju u rezultatu filtriranja. Bellova i trougaona funkcija pripadnosti pokazale su se kao najbolji izbor pri fazifikaciji parametara filtra. Algoritam je evaluiran nad više baza slika i postignuti su zadovoljavajući rezultati. Komponente od interesa su efikasno izdvojene i postignuta značajna otpornost na šum i degradaciju na slici.
The thesis presents a new algorithm for detection and extraction of license plates from a vehicle image using a fuzzy two-dimensional Gabor filter. The filter parameters, orientation and wavelengths are fuzzified to optimize the Gabor filter’s response and achieve a greater selectivity. It was concluded that Bell’s function and triangular membership function are the most efficient methods for fuzzification. Algorithm was evaluated on several databases and has provided satisfactory results. The components of interest were efficiently extracted, and the procedure was found to be very noise-resistant.
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Špaňhel, Jakub. "Re-identifikace vozidla pomocí rozpoznání jeho registrační značky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264932.

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This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.
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Li, Hui. "Text detection and recognition in natural scene images." Thesis, 2018. http://hdl.handle.net/2440/115175.

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This thesis addresses the problem of end-to-end text detection and recognition in natural scene images based on deep neural networks. Scene text detection and recognition aim to find regions in an image that are considered as text by human beings, generate a bounding box for each word and output a corresponding sequence of characters. As a useful task in image analysis, scene text detection and recognition attract much attention in computer vision field. In this thesis, we tackle this problem by taking advantage of the success in deep learning techniques. Car license plates can be viewed as a spacial case of scene text, as they both consist of characters and appear in natural scenes. Nevertheless, they have their respective specificities. During the research progress, we start from car license plate detection and recognition. Then we extend the methods to general scene text, with additional ideas proposed. For both tasks, we develop two approaches respectively: a stepwise one and an integrated one. Stepwise methods tackle text detection and recognition step by step by respective models; while integrated methods handle both text detection and recognition simultaneously via one model. All approaches are based on the powerful deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), considering the tremendous breakthroughs they brought into the computer vision community. To begin with, a stepwise framework is proposed to tackle text detection and recognition, with its application to car license plates and general scene text respectively. A character CNN classifier is well trained to detect characters from an image in a sliding window manner. The detected characters are then grouped together as license plates or text lines according to some heuristic rules. A sequence labeling based method is proposed to recognize the whole license plate or text line without character level segmentation. On the basis of the sequence labeling based recognition method, to accelerate the processing speed, an integrated deep neural network is then proposed to address car license plate detection and recognition concurrently. It integrates both CNNs and RNNs in one network, and can be trained end-to-end. Both car license plate bounding boxes and their labels are generated in a single forward evaluation of the network. The whole process involves no heuristic rule, and avoids intermediate procedures like image cropping or feature recalculation, which not only prevents error accumulation, but also reduces computation burden. Lastly, the unified network is extended to simultaneous general text detection and recognition in natural scene. In contrast to the one for car license plates, some innovations are proposed to accommodate the special characteristics of general text. A varying-size RoI encoding method is proposed to handle the various aspect ratios of general text. An attention-based sequence-to-sequence learning structure is adopted for word recognition. It is expected that a character-level language model can be learnt in this manner. The whole framework can be trained end-to-end, requiring only images, the ground-truth bounding boxes and text labels. Through end-to-end training, the learned features can be more discriminative, which improves the overall performance. The convolutional features are calculated only once and shared by both detection and recognition, which saves the processing time. The proposed method has achieved state-of-the-art performance on several standard benchmark datasets.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
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tsai, Sung-nien, and 蔡松年. "Dynamic Car License Plate Detection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/22083988530781627442.

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

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碩士
淡江大學
資訊工程學系碩士班
93
Embedded System is a computing platform designed for specific purpose. Because it’s task is much simpler than personal computer which is designed for general computing purpose, Embedded System can simplify its hardware architecture, cost down its hardware price, produce smaller device and low energy consumption. It also fits to be mobile computing platform. But Embedded System is constrained by its simple architecture; its processing power is much slower than personal computer. This paper purpose a software porting procedure between personal computer and Embedded System platform via a instance of Car License Plate Recognition System , and make Embedded Car License Plate Recognition System more efficient via exchange floating operation by integer operation and bitwise operation. For Example the image format captured by COMS, is Color Filter Array ,and this format will lost 2/3’s original image illumination. The illumination recovery process was originally involve with mass of floating operation, after applying the speeding method that we just mention before the processing time become times faster. We also use uClinux to assist hardware communication and process scheduling, and uClinux makes Embedded System able to handle complicated process control, also make software porting much smooth. Network File System ,NFS not only resolving the problem of lacking storage media, but also reduces the times of flash Rom burning procedure. Finally we use TCP/IP to transfer the image captured by CMOS to remote personal computer for Car License Plate Recognition ,and compare Car License Plate Recognition results between Embedded System and personal computer.
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Wang, Ching-Chung, and 王精忠. "THE STUDY OF CAR LICENSE PLATE RECOGNITION SYSTEM." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84668349117339820560.

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

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Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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Book chapters on the topic "Car license plate detection and recognition"

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Zhang, Wei, Yaobin Mao, and Yi Han. "SLPNet: Towards End-to-End Car License Plate Detection and Recognition Using Lightweight CNN." In Pattern Recognition and Computer Vision, 290–302. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60639-8_25.

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Caccia, Fabio, Roberto Marmo, and Luca Lombardi. "License Plate Detection and Character Recognition." In Image Analysis and Processing – ICIAP 2009, 471–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_51.

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Rahman, Md J., S. S. Beauchemin, and M. A. Bauer. "License Plate Detection and Recognition: An Empirical Study." In Advances in Intelligent Systems and Computing, 339–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_24.

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Negri, Pablo, Mariano Tepper, Daniel Acevedo, Julio Jacobo, and Marta Mejail. "Multiple Clues for License Plate Detection and Recognition." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 269–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_38.

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Islam, Tariqul, and Dm Mehedi Hasan Abid. "Automatic Vehicle Bangla License Plate Detection and Recognition." In Algorithms for Intelligent Systems, 523–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_44.

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Sharma, Vishal, Manvi Jain, Tanvi Jain, and Rashmi Mishra. "License Plate Detection and Recognition Using OpenCV–Python." In Lecture Notes in Electrical Engineering, 251–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8248-3_20.

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Silva, Sérgio Montazzolli, and Cláudio Rosito Jung. "License Plate Detection and Recognition in Unconstrained Scenarios." In Computer Vision – ECCV 2018, 593–609. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_36.

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Soghadi, Zahra Taleb, and Ching Y. Suen. "License Plate Detection and Recognition by Convolutional Neural Networks." In Pattern Recognition and Artificial Intelligence, 380–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_33.

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Li, Zhen-Jia, Song-Lu Chen, Qi Liu, Feng Chen, and Xu-Cheng Yin. "Anchor-Free Location Refinement Network for Small License Plate Detection." In Pattern Recognition and Computer Vision, 506–19. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18916-6_41.

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Vig, Simar, Archita Arora, and Greeshma Arya. "Automated License Plate Detection and Recognition Using Deep Learning." In Advancements in Interdisciplinary Research, 419–31. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_39.

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Conference papers on the topic "Car license plate detection and recognition"

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Ahn, Chi-Sung, Bong-Gyou Lee, Seung-Su Yang, and Seok-Cheon Park. "Design of car license plate area detection algorithm for enhanced recognition plate." In 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). IEEE, 2017. http://dx.doi.org/10.1109/caipt.2017.8320749.

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Li, Wei-Chen, Ting-Hsuan Hsu, Ke-Nung Huang, and Chou-Chen Wang. "A YOLO-Based Method for Oblique Car License Plate Detection and Recognition." In 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2021. http://dx.doi.org/10.1109/snpd51163.2021.9704935.

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Han, Mengcheng, and Yu Sun. "An Intelligent Mobile Application for Car License Plate Detection and Analysis using Machine Learning Algorithm." In 9th International Conference on Artificial Intelligence and Applications (AIAP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120406.

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How did image recognition and object analysis function to bring convenience to people’s lives? Within this question I bear in my mind, I started to explore and build this Automatic Car License Plate Detect and Analyze Project. Since cameras are being widely used for recording and analyzing vehicle information, it has been a great cost to buy such intelligent devices. Guided by recent research on machine learning approaches , we solve this financial problem by designing and implementing a mobile phone application that automatically utilizes the camera installed on the phone to analyze the information of the car license plate. Our design is built to provide users with an accessible way to analyze license plates in complex environments.
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Tom, Ruben Jose, Awanit Kumar, Syed Basha Shaik, Lydia D. Isaac, Vikas Tripathi, and Prakash Pareek. "Car License Plate Detection and Recognition Using Modified U-Net Deep Learning Model." In 2022 8th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2022. http://dx.doi.org/10.1109/icsss54381.2022.9782176.

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Pinto, Pedro Ferreira Alves, Antonio José G. Busson, João P. Forny de Melo, Sérgio Colcher, and Ruy Luiz Milidiú. "PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8149.

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Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.
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Zhong, Yuwen, Yonggui Liu, Fei Luo, and Hang Zhang. "A Novel Integrated Neural Network for License Plate Detection And Recognition." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326612.

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Saadouli, Ghaida, Maha Ibrahim Elburdani, Razan Mohammed Al-Qatouni, Suchithra Kunhoth, and Somaya Al-Maadeed. "Automatic and Secure Electronic Gate System Using Fusion of License Plate, Car Make Recognition and Face Detection." In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). IEEE, 2020. http://dx.doi.org/10.1109/iciot48696.2020.9089615.

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Omran, Safaa, and Jumana Jarallah. "Iraqi Car License Plate Recognition Using OCR." In 2nd International Conference of Cihan University-Erbil on Communication Engineering and Computer Science. Cihan University-Erbil, 2017. http://dx.doi.org/10.24086/cocos17.19.

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Omran, Safaa S., and Jumana A. Jarallah. "Iraqi car license plate recognition using OCR." In 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT). IEEE, 2017. http://dx.doi.org/10.1109/ntict.2017.7976127.

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Chai, Hum Yan, Hon Hock Woon, Liang Kim Meng, and Yuen Shang Li. "Non-standard Malaysian car license plate recognition." In 2014 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2014. http://dx.doi.org/10.1109/iscaie.2014.7010228.

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