Corrected some grammatical errors.

docs/verslag.tex

@@ -39,8 +39,8 @@ Microsoft recently published a new and effective method to find the location of
 text in an image.
 
 Determining what character we are looking at will be done by using Local Binary
-Patterns. The main goal of our research is finding out how effective LBPs are
-in classifying characters on a licenseplate.
+Patterns. The main goal of our research is finding out how effective LBP's are
+in classifying characters on a license plate.
 
 In short our program must be able to do the following:
 
@@ -145,9 +145,9 @@ stored in XML files. So, the first step is to read these XML files.\\
 
 
 \paragraph*{Perspective transformation}
-Once we retrieved the cornerpoints of the licenseplate, we feed those to a
-module that extracts the (warped) licenseplate from the original image, and
-creates a new image where the licenseplate is cut out, and is transformed to a
+Once we retrieved the cornerpoints of the license plate, we feed those to a
+module that extracts the (warped) license plate from the original image, and
+creates a new image where the license plate is cut out, and is transformed to a
 rectangle.
 
 \subsection{Noise reduction}
@@ -157,11 +157,11 @@ etc., as from dirt on the license plate. In this case, noise therefore means
 any unwanted difference in color from the surrounding pixels.
 
 \paragraph*{Camera noise and small amounts of dirt}
-The dirt on the licenseplate can be of different sizes. We can reduce the 
+The dirt on the license plate can be of different sizes. We can reduce the 
 smaller amounts of dirt in the same way as we reduce normal noise, by applying
-a gaussian blur to the image. This is the next step in our program.\\
+a Gaussian blur to the image. This is the next step in our program.\\
 \\
-The gaussian filter we use comes from the \texttt{scipy.ndimage} module. We use
+The Gaussian filter we use comes from the \texttt{scipy.ndimage} module. We use
 this function instead of our own function, because the standard functions are
 most likely more optimized then our own implementation, and speed is an
 important factor in this application.
@@ -179,7 +179,7 @@ surrounding the character.
 
 The retrieval of the character is done the same as the retrieval of the license
 plate, by using a perspective transformation. The location of the characters on
-the licenseplate is also available in de XML file, so this is parsed from that
+the license plate is also available in de XML file, so this is parsed from that
 as well.
 
 \subsection{Creating Local Binary Patterns and feature vector}
@@ -200,12 +200,12 @@ available. These parameters are:\\
 \begin{tabular}{l|l}
 	Parameter 			& Description\\
 	\hline
-	$\sigma$  			& The size of the gaussian blur.\\
+	$\sigma$  			& The size of the Gaussian blur.\\
 	\emph{cell size}	& The size of a cell for which a histogram of LBPs will
 	                      be generated.\\
 	$\gamma$			& Parameter for the Radial kernel used in the SVM.\\
 	$c$					& The soft margin of the SVM. Allows how much training
-						  errors are excepted.
+						  errors are accepted.
 \end{tabular}\\
 \\
 For each of these parameters, we will describe how we searched for a good
@@ -225,10 +225,10 @@ that this was $\sigma = ?$.
 The cell size of the Local Binary Patterns determines over what region a
 histogram is made. The trade-off here is that a bigger cell size makes the
 classification less affected by relative movement of a character compared to
-those in the learningset, since the important structure will be more likely to
+those in the learning set, since the important structure will be more likely to
 remain in the same cell. However, if the cell size is too big, there will not
 be enough cells to properly describe the different areas of the character, and
-the featurevectors will not have enough elements.\\
+the feature vectors will not have enough elements.\\
 \\
 In order to find this parameter, we used a trial-and-error technique on a few
 basic cell sizes, being ?, 16, ?. We found that the best result was reached by
@@ -257,15 +257,15 @@ We found that the best values for these parameters are $c=?$ and $\gamma =?$.
 
 \section{Results}
 
-The wanted to find out two things with this research: The speed of the
+The goal was to find out two things with this research: The speed of the
 classification and the accuracy. In this section we will show our findings.
 
 \subsection{Speed}
 
-Recognizing license plates is something that has to be done with good speed,
-since there can be a lot of cars passing a camera, especially on a highway.
+Recognizing license plates is something that has to be done fast, since there
+can be a lot of cars passing a camera in a short time, especially on a highway.
 Therefore, we measured how well our program performed in terms of speed. We
-measure the time used to classify a license plate, not the trainign of the
+measure the time used to classify a license plate, not the training of the
 dataset, since that can be done offline, and speed is not a primary necessity
 there.\\
 \\
@@ -275,13 +275,13 @@ The speed of a classification turned out to be blablabla.
 
 Of course, it is vital that the recognition of a license plate is correct,
 almost correct is not good enough here. Therefore, we have to get the highest
-accuracy score we possibly can. According to Wikipedia
+accuracy score we possibly can.\\
+\\ According to Wikipedia
 \footnote{
 \url{http://en.wikipedia.org/wiki/Automatic_number_plate_recognition}},
 commercial license plate recognition software score about $90\%$ to $94\%$,
-under optimal conditions and with modern equipment.\\
-\\
-Our program scores an average of blablabla.
+under optimal conditions and with modern equipment. Our program scores an
+average of blablabla.
 
 \section{Conclusion}