Vehicle license plate detection and recognition

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Vehicle license plate detection and recognition

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/10564

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dc.contributor.advisor He, Zhihai, 1973- en_US
dc.contributor.author Li, Xin, 1984- en_US
dc.date.accessioned 2011-04-25T14:32:02Z
dc.date.available 2011-04-25T14:32:02Z
dc.date.issued 2010 en_US
dc.date.submitted 2010 Fall en_US
dc.identifier.other LiX-120910-T789 en_US
dc.identifier.uri http://hdl.handle.net/10355/10564
dc.description Title from PDF of title page (University of Missouri--Columbia, viewed on March 30, 2011). en_US
dc.description The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. en_US
dc.description Thesis advisor: Dr. Zhihai He. en_US
dc.description Includes bibliographical references. en_US
dc.description M.S. University of Missouri--Columbia 2010. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Computer engineering. en_US
dc.description.abstract In this work, we develop a license plate detection and recognition 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. A car head and rear detection method was also proposed to accelerate the time consuming scanning process. A comparison of the performance for different cell and block sizes of HOG feature is provided, and four rounds of bootstrapping was performed to achieve better detection performance. 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 the Caltech data set (1999), and achieved a detection rate of 96.0%. We also studied how its performance is impacted by different levels of noise and motion blur. After license plate detection, we proceed to perform character segmentation and recognition using SVM classifiers with HOG features. In character segmentation, we need to deal with low contrast and tilted plates. The system performs window searching in different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using Mean Shift. en_US
dc.format.extent vii, 61 pages en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2010 Freely available theses (MU) en_US
dc.subject.lcsh Automobile license plates en_US
dc.subject.lcsh Vehicle detectors en_US
dc.subject.lcsh Support vector machines en_US
dc.subject.lcsh Motor vehicles -- Automatic location systems en_US
dc.title Vehicle license plate detection and recognition en_US
dc.type Thesis en_US
thesis.degree.discipline Computer engineering en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name M.S. en_US
thesis.degree.level Masters en_US
dc.identifier.merlin b82191918
dc.identifier.oclc 711898125 en_US
dc.relation.ispartofcommunity University of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2010 Theses


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