Making the text more constant

mini_proj/report/waldo.tex

@@ -194,21 +194,20 @@
 
 		The Waldo image database consists of images of size	64$\times$64,
 		128$\times$128, and 256$\times$256 pixels obtained by dividing complete
-		Where's Waldo? puzzles. Within each set of images, those containing Waldo
-		are located in a folder called `waldo', and those not containing Waldo, in a
-		folder called `not\_waldo'. Since Where's Waldo? puzzles are usually densely
-		populated and contain fine details, the 64$\times$64 pixel set of images
-		were selected to train and evaluate the machine learning models. These
-		images provide the added benefit of containing the most individual images of
-		the three size groups. \\
+		``Where's Waldo?'' puzzles. Within each set of images, those containing
+		Waldo are located in a folder called \texttt{waldo}, and those not containing
+		Waldo, in a folder called \texttt{not\_waldo}. Since ``Where's Waldo?''
+		puzzles are usually densely populated and contain fine details, the
+		64$\times$64 pixel set of images were selected to train and evaluate the
+		machine learning models. These images provide the added benefit of
+		containing the most individual images of the three size groups. \\
 
 		Each of the 64$\times$64 pixel images were inserted into a
 		Numpy~\cite{numpy} array of images, and a binary value was inserted into a
-		seperate list at the same index. These binary values form the labels for
-		each image (waldo or not waldo). Colour normalisation was performed on each
-		so that artefacts in an image's colour profile correspond to meaningful
-		features of the image (rather than photographic method).\\
-
+		separate list at the same index. These binary values form the labels for
+		each image (``Waldo'' or ``not Waldo''). Color normalization was performed
+		on each so that artifacts in an image's color profile correspond to
+		meaningful features of the image (rather than photographic method).\\
 
 		Each original puzzle is broken down into many images, and only contains one
 		Waldo. Although Waldo might span multiple 64$\times$64 pixel squares, this
@@ -222,27 +221,27 @@
 		methods by exposing each technique to variations of the image during the
 		training phase. \\
 
-		Despite the additional data, there were still over ten times as many
-		non-Waldo images than Waldo images. Therefore, it was necessary to cull the
-		no-Waldo data, so that there was an even split of Waldo and non-Waldo
-		images, improving the representation of true positives in the image data
-		set. Following preprocessing, the images (and associated labels) were
-		divided into a training and a test set with a 3:1 split. \\
+		Despite the additional data, there were still ten times more ``non-Waldo''
+		images than Waldo images. Therefore, it was necessary to cull the
+		``non-Waldo'' data, so that there was an even split of ``Waldo'' and
+		``non-Waldo'' images, improving the representation of true positives in the
+		image data set. Following preprocessing, the images (and associated labels)
+		were divided into a training and a test set with a 3:1 split. \\
 
 		\subsection{Neural Network Training}\label{nnTraining}
 
 		The neural networks used to classify the images were supervised learning
 		models; requiring training on a dataset of typical images. Each network was
-		trained using the preprocessed training dataset and labels, for 25 epochs
+		trained using the preprocessed training dataset and labels for 25 epochs
 		(one forward and backward pass of all data) in batches of 150. The number of
-		epochs was chosen to maximise training time and prevent overfitting of the
+		epochs was chosen to maximize training time and prevent overfitting of the
 		training data, given current model parameters. The batch size is the number
 		of images sent through each pass of the network. Using the entire dataset
 		would train the network quickly, but decrease the network's ability to learn
 		unique features from the data. Passing one image at a time may allow the
 		model to learn more about each image, however it would also increase the
 		training time and risk of overfitting the data. Therefore the batch size was
-		chosen to maintain training accuracy while minimising training time.
+		chosen to maintain training accuracy while minimizing training time.
 
 		\subsection{Neural Network Testing}\label{nnTesting}