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\title{Teaching a computer to learn, find and read licence plates}
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\date{November 17th, 2011}
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+% Paragraph indentation
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+\setlength{\parindent}{0pt}
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+\setlength{\parskip}{1ex plus 0.5ex minus 0.2ex}
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\begin{document}
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\maketitle
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@@ -15,50 +19,62 @@ Gijs van der Voort\\Richard Torenvliet\\Jayke Meijer\\Tadde\"us Kroes\\Fabi\'en
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\section{Introduction}
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Licence plates are used all over the world. The plates are, usually, attached to the front and rear
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-of a motorised vehicle and used for indentifying this vehicle. Every
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+of a motorised vehicle and used for identifying this vehicle. Every
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country can have more or less its own version of a licence plate, but all these systems do not
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-differ greatly. We will be focusing on the dutch system for licence plates.
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+differ greatly. We will be focusing on the Dutch system for licence plates.
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\section{Problem Description}
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-License plates are used for indentification and thus made to recognize from great
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+License plates are used for identification and thus made to recognize from great
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distances and still be seen in many weather conditions. Our learning set of photos contains
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-''' ik weet niet precies wat voor camera ''. The angle in which these pictures are taken or the angle
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+'' ik weet niet precies wat voor camera ''. The angle in which these pictures are taken or the angle
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of the approaching vehicles are always different and some licence plates are a bit dirty,
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-but for a human they are still pretty easy to indentify. A computer or perhaps a small
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-chipset will need to be thourougly practiced. In short our program must be able to
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+but for a human they are still pretty easy to identify. A computer or perhaps a small
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+chipset will need to be thoroughly practiced. In short our program must be able to
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do the following:
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-\begin{itemize}
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+\begin{enumerate}
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\item Find the location of the license plate.
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-\item Use transformations so it gets an upfront view.
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+\item Use perspective transformations to obtain an upfront view.
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\item Reduce noise where possible.
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-\item Get the locations of each letter and extracting it.
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-\item Apply a local binary pattern algorithm on each letter.
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-\item Matching the found patterns with found results and return the best match.
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-\end{itemize}
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+\item Find the locations of each letter and extract it.
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+\item Apply a Local Binary Pattern algorithm on each letter.
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+\item Match the found patterns with results from the learning set and return the best match for each letter.
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+\end{enumerate}
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\section{Solution}
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Now that we know the problem we can start with stating our solution. This will
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-come in a few steps aswell.
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+come in a few steps as well.
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\subsection{Localizing the plate}
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The photos are of very high contrast. Most of the time only the lights of a vehicle
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-are visible in addition to the license plate. We can first crop the image untill
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+are visible in addition to the license plate. We can first crop the image until
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it finds brighter pixel values in a row or column. Then we can apply ''?? weet niet hoor'' local histogram
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matching to find out whether we have a light or license plate.
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\subsection{Transformations}
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-Affine transformations will do the trick
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+Once the locations of the four corner points of the license plate have been
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+found, a simple perspective transformation will be sufficient to transform and
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+resize the plate to a normalized format.
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\subsection{Reducing noise}
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Weet niet precies hoe, maar van die kleine rondjes / vlekjes / stipjes moeten
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we wel een beetje weghalen want die maken het wel een beetje lelijk
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+Taddeus: Ik brainstorm hier een beetje...:
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+
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+Small amounts of noise will probably be suppressed by usage of a Gaussian
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+filter. A real problem occurs in very dirty licence plates, where branches and
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+dirt over a letter could radically change the local binary pattern. A question
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+we can ask ourselves here, is whether we want to concentrate ourselves on
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+these exceptional cases. By law, license plates have to be readable.
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+Therefore, we will first direct our attention at getting a higher score in the
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+'regular' test set before addressing these cases.
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+
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\subsection{Extracting a letter}
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De karakteristiek bepalen van het dash/streepje (-) dan heb je in elk geval al
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