diff --git a/body.tex b/body.tex
index a761dd0..96d616e 100644
--- a/body.tex
+++ b/body.tex
@@ -590,7 +590,7 @@ outlined, followed by the implementation of the auto-encoder.
 
 The source code of many published papers is either not available
 or seems like an afterthought: it is poorly documented, difficult
-to integrate in your own work, and often does not follow common
+to integrate into your own work, and often does not follow common
 software development best practices. Moreover, with Tensorflow,
 PyTorch, and Caffe there are at least three machine learning
 frameworks. Every research team seems to prefer another framework
@@ -626,15 +626,16 @@ need to be removed.
 For the MS COCO data set, all annotations were checked for
 impossible values: bounding box height or width lower than zero,
 x1 and y1 bounding box coordinates lower than zero,
-x2 and y2 coordinates lower or equal to zero, x1 greater than x2,
+x2 and y2 coordinates lower than or equal to zero, x1 greater than x2,
 y1 greater than y2, image width lower than x2,
 and image height lower than y2. In the last two cases the
-bounding box width or height was set to (image with - x1) or (image height - y1)
-respectively; in the other cases the annotation was skipped.
+bounding box width or height was set to (image with - x1) or
+(image height - y1) respectively;
+in the other cases the annotation was skipped.
 If the bounding box width or height afterwards is
-lower or equal to zero the annotation is skipped.
+lower than or equal to zero the annotation is skipped.
 
-In this thesis SceneNet RGB-D is always used with COCO classes.
+In this thesis, SceneNet RGB-D is always used with COCO classes.
 Therefore, a mapping between COCO and SceneNet RGB-D and vice versa
 was necessary. It was created my manually going through each
 Wordnet ID and searching for a fitting COCO class.