diff --git a/body.tex b/body.tex
index 6b01d03..54a762a 100644
--- a/body.tex
+++ b/body.tex
@@ -8,7 +8,7 @@ providing technical details.
 \subsection*{Motivation}
 
 Famous examples like the automatic soap dispenser which does not
-recognize the hand of a black person but dispenses soap when presented
+recognise the hand of a black person but dispenses soap when presented
 with a paper towel raise the question of bias in computer
 systems~\cite{Friedman1996}. Related to this ethical question regarding
 the design of so called algorithms is the question of
@@ -48,7 +48,7 @@ class of any given input. In this thesis, I will work with both.
     were not present during training time.
     Icons in this image have been taken from the COCO data set
     website (\url{https://cocodataset.org/\#explore}) and were
-    vectorized afterwards. Resembles figure 1 of Miller et al.~\cite{Miller2018}.}
+    vectorised afterwards. Resembles figure 1 of Miller et al.~\cite{Miller2018}.}
     \label{fig:open-set}
 \end{figure}
 
@@ -73,7 +73,7 @@ Therefore it would be impossible for them to identify the output
 of the network as false positive.
 
 This goes back to the need for automatic explanation. Such a system
-should by itself recognize that the given object is unknown and
+should by itself recognise that the given object is unknown and
 hence mark any classification result of the network as meaningless.
 Technically there are two slightly different approaches that deal
 with this type of task: model uncertainty and novelty detection.
@@ -123,7 +123,7 @@ without further fine-tuning on the SceneNet RGB-D data
 set~\cite{McCormac2017} and reported good results regarding
 open set error for an SSD variant with dropout sampling and entropy
 thresholding.
-If their results are generalizable it should be possible to replicate
+If their results are generalisable it should be possible to replicate
 the relative difference between the variants on the COCO data set.
 This leads to the following hypothesis: \emph{Dropout sampling
 delivers better object detection performance under open set