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@@ -22,12 +22,12 @@ Fabi\'en Tesselaar
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\section{Problem description}
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-license plates are used for uniquely identifying motorized vehicles and are
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+License plates are used for uniquely identifying motorized vehicles and are
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made to be read by humans from great distances and in all kinds of weather
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conditions.
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Reading license plates with a computer is much more difficult. Our dataset
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-contains photographs from license plates from all sorts of angles and distance.
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+contains photographs from license plates from all sorts of angles and distances.
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Meaning that not only do we have to implement a method to read the actual
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characters, but also have to determine the location of the license plate and its
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transformation due to different angles.
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@@ -50,7 +50,7 @@ In short our program must be able to do the following:
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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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+Now that the problem is defined, the next step is stating a solution. This will
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come in a few steps as well.
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\subsection{Transformation}
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@@ -68,14 +68,16 @@ 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. Therefore,
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we will first direct our attention at getting a higher score in the 'regular'
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test set before addressing these cases. Looking at how LBP work, there is a good
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-change that our features are, to a certain degree, indifferent to noise on the
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-plates.
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+change that our features are indifferent to noise to a certain degree on the
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+licence plates.
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\subsection{Extracting a letter}
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-Because we are already given the locations of the characters, we only need to
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-transform those locations using the same perspective transform used to to
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-create a front facing license plate.
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+Because the locations of the characters are already given, the next step is to
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+transform the characters to a normalized manner. The letter W is used as a
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+fixing point to normalize the width of all the characters, because W is the
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+broadest character of the alphabet. Also the height of the characters are
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+normalized but its depending on a trial and error fase.
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\begin{enumerate}
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\item Crop the image in such a way that the character precisely fits the image.
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@@ -84,12 +86,12 @@ create a front facing license plate.
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\end{enumerate}
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The resulting image will always have the same size, the character contained will
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-always be of the same height, and the character will alway be positioned at
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+always be of the same height, and the character will always be positioned at
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either the left of right side of the image.
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\subsection{Local binary patterns}
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-Once we have separate digits and characters, we intend to use Local Binary
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+Once separate digits and characters are found, we intend to use Local Binary
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Patterns to determine what character or digit we are dealing with. Local Binary
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Patters are a way to classify a texture, because it can create a histogram which
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describes the distribution of line directions in the image. Since letters on a
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@@ -111,7 +113,7 @@ the image.
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\subsection{Matching the database}
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-In order to determine what character we are dealing with, we use a SVM, as said
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+In order to determine what characters we are dealing with, we use a SVM, as said
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before. To prevent us from having to teach this SVM each time we start the
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program, we are going to save the SVM to a pickle object, which packs an object
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in Python to a certain data format, so it can be unpacked somewhere else, or, in
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Richard Torenvliet