Spell- and sentence-checked plan.tex.

.gitignore

@@ -6,3 +6,4 @@
 *.log
 *.synctex.gz
 *.toc
+*.out

docs/plan.tex

@@ -1,5 +1,7 @@
 \documentclass[a4paper]{article}
 
+\usepackage{hyperref}
+
 \title{Using local binary patterns to read license plates in photographs}
 \date{November 17th, 2011}
 
@@ -22,30 +24,31 @@ Fabi\'en Tesselaar
 
 \section{Problem description}
 
-license plates are used for uniquely identifying motorized vehicles and are 
-made to be read by humans from great distances and in all kinds of weather 
+License plates are used for uniquely identifying motorized vehicles and are
+made to be read by humans from great distances and in all kinds of weather
 conditions.
 
 Reading license plates with a computer is much more difficult. Our dataset
-contains photographs from license plates from all sorts of angles and distance. 
-Meaning that not only do we have to implement a method to read the actual 
-characters, but also have to determine the location of the license plate and its
-transformation due to different angles.
-
-We will focus our research in reading the transformed characters of the 
-lisence plate, on which we know where the letters are located. This is because
-Microsoft recently published a new and effective method to find the location of 
+contains photographs of license plates from various angles and distances. This
+means that not only do we have to implement a method to read the actual
+characters, but also have to determine the location of the license plate and
+its transformation due to different angles.
+
+We will focus our research on reading the transformed characters on the
+license plate, of which we know where the letters are located. This is because
+Microsoft recently published a new and effective method to find the location of
 text in an image.
 
 In short our program must be able to do the following:
 
 \begin{enumerate}
-\item Use perspective transformation to obtain an upfront view of license plate.
-\item Reduce noise where possible.
-\item Extract each character using the location points in the info file.
-\item Transform character to a normal form. 
-\item Create a local binary pattern histogram vector.
-\item Match the found vector with the learning set.
+    \item Use perspective transformation to obtain an upfront view of license
+          plate.
+    \item Reduce noise where possible.
+    \item Extract each character using the location points in the info file.
+    \item Transform character to a normal form.
+    \item Create a local binary pattern histogram vector.
+    \item Match the found vector with a learning set.
 \end{enumerate}
 
 \section{Solution}
@@ -55,66 +58,70 @@ come in a few steps as well.
 
 \subsection{Transformation}
 
-A simple perspective transformation will be sufficient to transform and resize 
-the plate to a normalized format. Corners of license plates can be found in the
-data files.
+A simple perspective transformation will be sufficient to transform and resize
+the plate to a normalized format. The corner positions of license plates in the
+dataset are supplied together with the dataset.
 
 \subsection{Reducing noise}
 
 Small amounts of noise will probably be suppressed by usage of a Gaussian
 filter. A real problem occurs in very dirty license plates, where branches and
 dirt over a letter could radically change the local binary pattern. A question
-we can ask ourselves here, is whether we want to concentrate ourselves on
-these exceptional cases. By law, license plates have to be readable. Therefore, 
-we will first direct our attention at getting a higher score in the 'regular' 
-test set before addressing these cases. Looking at how LBP work, there is a good 
-change that our features are, to a certain degree, indifferent to noise on the 
-plates.
+we can ask ourselves here, is whether we want to concentrate ourselves on these
+exceptional cases. By law, license plates have to be readable. Therefore, we
+will first direct our attention at getting a higher score in the 'regular' test
+set before addressing these cases. Considered the fact that the LBP algorithm
+divides a letter into a lot of cells, there is a good change that a great
+number of cells will still match the learning set, and thus still return the
+correct character as a best match. Therefore, we expect the algorithm to be
+very robust when dealing with noisy images.
 
 \subsection{Extracting a letter}
 
-Because we are already given the locations of the characters, we only need to 
-transform those locations using the same perspective transform used to to
+Because we are already given the locations of the characters, we only need to
+transform those locations using the same perspective transformation used to
 create a front facing license plate.
 
 \begin{enumerate}
-\item Crop the image in such a way that the character precisely fits the image.
-\item Scale the image to a standard height.
-\item Extend the image on either the left or right side to a certain width. 
+    \item Crop the image in such a way that the character precisely fits the
+          image.
+    \item Scale the image to a standard height.
+    \item Extend the image on either the left or right side to a certain width.
 \end{enumerate}
 
-The resulting image will always have the same size, the character contained will
-always be of the same height, and the character will alway be positioned at 
-either the left of right side of the image.
+The resulting image will always have the same size, the character contained
+will always be of the same height, and the character will alway be positioned
+at either the left of right side of the image.
 
 \subsection{Local binary patterns}
 
-Once we have separate digits and characters, we intend to use Local Binary 
-Patterns to determine what character or digit we are dealing with. Local Binary 
-Patters are a way to classify a texture, because it can create a histogram which
-describes the distribution of line directions in the image. Since letters on a 
-license plate are mainly build up of straight lines and simple curves, it should 
-theoretically be possible to identify these using Local Binary Patterns.
+Once we have separate digits and characters, we intend to use Local Binary
+Patterns to determine what character or digit we are dealing with. Local Binary
+Patters are a way to classify a texture, because it can create a histogram
+which describes the distribution of line directions in the image. Since letters
+on a license plate are mainly build up of straight lines and simple curves, it
+should theoretically be possible to identify these using Local Binary Patterns.
 
-This will actually be the first thing we implement, since it is not known if it 
-will give the desired results. Our first goal is therefore a proof of concept 
-that using LBP's is a good way to determine which character we are dealing with.
+This will actually be the first thing we implement, since it is not known if it
+will give the desired results. Our first goal is therefore a proof of concept
+that using LBP's is a good way to determine which character we are dealing
+with.
 
-Important to note is that by now, we have transformed this letter to a standard 
-size, which eliminates the need to normalize the histograms generated by the 
+Important to note is that by now, we have transformed this letter to a standard
+size, which eliminates the need to normalize the histograms generated by the
 algorithm.
 
-Once we have a Local Binary Pattern of the character, we use a Support Vector 
-Machine to determine what letter we are dealing with. For this, the feature 
-vector of the image will be a concatenation of the histograms of the cells in 
+Once we have a Local Binary Pattern of the character, we use a Support Vector
+Machine to determine what letter we are dealing with. For this, the feature
+vector of the image will be a concatenation of the histograms of the cells in
 the image.
 
 \subsection{Matching the database}
 
-In order to determine what character we are dealing with, we use a SVM, as said 
-before. To prevent us from having to teach this SVM each time we start the 
-program, we are going to save the SVM to a pickle object, which packs an object 
-in Python to a certain data format, so it can be unpacked somewhere else, or, in 
-our case, when executing the program to identify a character.
+In order to recognize what character we are dealing with, we use a Support
+Vector Machine. The SVM can be trained with a subsection of the given dataset
+called the ''Learning set''. Once trained, the entire classifier can be saved
+as a Pickle object\footnote{See
+\url{http://docs.python.org/library/pickle.html}} for later usage.
 
 \end{document}