diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex
index 84b2494..4492253 100644
--- a/mini_proj/report/waldo.tex
+++ b/mini_proj/report/waldo.tex
@@ -46,8 +46,7 @@
 		\end{abstract}
 
 		\section{Introduction}
-
-		Almost every child around the world knows about ``Where's Waldo?'', also
+		\tab Almost every child around the world knows about ``Where's Waldo?'', also
 		known as ``Where's Wally?'' in some countries. This famous puzzle book has
 		spread its way across the world and is published in more than 25 different
 		languages. The idea behind the books is to find the character Waldo,
@@ -59,7 +58,7 @@
 		that even adults will have trouble spotting Waldo is the fact that the
 		pictures are full of ``Red Herrings'': things that look like (or are colored
 		as) Waldo, but are not actually Waldo.
-
+		\\
 		\begin{figure}[ht]
 			\includegraphics[scale=0.35]{waldo.png}
 			\centering
@@ -69,7 +68,7 @@
 			}
 			\label{fig:waldo}
 		\end{figure}
-
+		\par
 		The task of finding Waldo is something that relates to a lot of real life
 		image recognition tasks. Fields like mining, astronomy, surveillance,
 		radiology, and microbiology often have to analyse images (or scans) to find
@@ -77,7 +76,8 @@
 		are especially hard when the thing(s) you are looking for are similar to the
 		rest of the images. These tasks are thus generally performed using computers
 		to identify possible matches.
-
+		\\
+		\par
 		``Where's Waldo?'' offers us a great tool to study this kind of problem in a
 		setting that is humanly tangible. In this report we will try to identify
 		Waldo in the puzzle images using different classification methods. Every
@@ -93,7 +93,7 @@
 
 		\section{Background} \label{sec:background}
 
-		The classification methods used can separated into two separate groups:
+		\tab The classification methods used can separated into two separate groups:
 		classical machine learning methods and neural network architectures. Many of
 		the classical machine learning algorithms have variations and improvements
 		for various purposes; however, for this report we will be using their only
@@ -103,7 +103,7 @@
 
 		\subsection{Classical Machine Learning Methods}
 
-		The following paragraphs will give only brief descriptions of the different
+		\tab The following paragraphs will give only brief descriptions of the different
 		classical machine learning methods used in this reports. For further reading
 		we recommend reading ``Supervised machine learning: A review of
 		classification techniques'' \cite{MLReview}.
@@ -158,8 +158,7 @@
 		of randomness and the mean of these trees is used which avoids this problem.
 
 		\subsection{Neural Network Architectures}
-
-		There are many well established architectures for Neural Networks depending
+		\tab There are many well established architectures for Neural Networks depending
 		on the task being performed. In this paper, the focus is placed on
 		convolution neural networks, which have been proven to effectively classify
 		images \cite{NIPS2012_4824}. One of the pioneering works in the field, the
@@ -185,7 +184,7 @@
 
 		\section{Method} \label{sec:method}
 
-		In order to effectively utilize the aforementioned modeling and
+		\tab In order to effectively utilize the aforementioned modeling and
 		classification techniques, a key consideration is the data they are acting
 		on. A dataset containing Waldo and non-Waldo images was obtained from an
 		Open Database\footnote{``The Open Database License (ODbL) is a license
@@ -238,7 +237,7 @@
 
 		\subsection{Neural Network Training}\label{nnTraining}
 
-		The neural networks used to classify the images were supervised learning
+		\tab 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
 		(one forward and backward pass of all data) in batches of 150. The number of
@@ -258,14 +257,14 @@
 		% Kelvin Start
 		\subsection{Benchmarking}\label{benchmarking}
 
-		In order to benchmark the Neural Networks, the performance of these
+		\tab In order to benchmark the Neural Networks, the performance of these
 		algorithms are evaluated against other Machine Learning algorithms. We use
 		Support Vector Machines, K-Nearest Neighbors (\(K=5\)), Naive Bayes and
 		Random Forest classifiers, as provided in Scikit-Learn~\cite{scikit-learn}.
 
 		\subsection{Performance Metrics}\label{performance-metrics}
 
-		To evaluate the performance of the models, we record the time taken by
+		\tab To evaluate the performance of the models, we record the time taken by
 		each model to train, based on the training data and the accuracy with which
     the model makes predictions. We calculate accuracy as
 		\[a = \frac{|correct\ predictions|}{|predictions|} = \frac{tp + tn}{tp + tn + fp + fn}\]
@@ -275,7 +274,7 @@
 
 		\section{Results} \label{sec:results}
 
-		The time taken to train each of the neural networks and traditional
+		\tab The time taken to train each of the neural networks and traditional
 		approaches was measured and recorded alongside their accuracy (evaluated
 		using a separate test dataset) in Table \ref{tab:results}.