Spell- and sentence-checked plan.tex.

.gitignore

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

docs/plan.tex

 \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 
+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.
+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 in reading the transformed characters of the 
-lisence plate, on which we know where the letters are located. This is because
+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}
@@ -56,49 +59,53 @@ 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.
+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
+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.
+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.
+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
@@ -111,10 +118,10 @@ 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}