diff --git a/misc/PlotCanvas.py b/misc/PlotCanvas.py
index 0c4b1c6c09fa4248b9c0d372768bec8b238ea105..52eec484e319cef0079f6ad2280d87fa4226620c 100644
--- a/misc/PlotCanvas.py
+++ b/misc/PlotCanvas.py
@@ -399,7 +399,8 @@ class PlotCanvas(FigureCanvas):
      
          if xtemp:
              for i in range(0,len(xtemp)):
-                 ann = self.ax.annotate(str(int(vals[i])), (xtemp[i], ytemp[i]))
+                 ann = self.ax.annotate(str(int(vals[i])), (xtemp[i], ytemp[i]),
+                                          ha='center', va='center')
                  self.ann_list.append(ann)
                  
          self.draw()
@@ -422,7 +423,8 @@ class PlotCanvas(FigureCanvas):
      
          if xtemp:
              for i in range(0,len(xtemp)):
-                  ann = self.ax2.annotate(str(vals[i]), (xtemp[i], ytemp[i]))
+                  ann = self.ax2.annotate(str(vals[i]), (xtemp[i], ytemp[i]),
+                                          ha='center', va='center')
                   self.ann_list_prev.append(ann)
          self.draw()
              
@@ -443,7 +445,8 @@ class PlotCanvas(FigureCanvas):
                  
          if xtemp:
              for i in range(0,len(xtemp)):
-                 ann = self.ax3.annotate(str(vals[i]), (xtemp[i], ytemp[i]))
+                 ann = self.ax3.annotate(str(vals[i]), (xtemp[i], ytemp[i]),
+                                          ha='center', va='center')
                  self.ann_list_next.append(ann)
          self.draw()
         
diff --git a/readme.md b/readme.md
index 23739c511419fdc31ec1b2c3667875291f6821c6..d9100c664d73d36f98f1257ba5e68a477314c4cc 100644
--- a/readme.md
+++ b/readme.md
@@ -2,17 +2,14 @@
 
 This is the user manual for the graphical interface for yeast segmentation using a state of the art convolutional neural network with U-Net architecture. As for now, the convolutional neural network is trained for phase contrast images. Compatability with brightfield images will come soon. You can find the training set with the annotated images here: https://www.epfl.ch/labs/lpbs/data-and-software/. 
 
+Want to try out the neural network first? Check out our online segmentation tool at https://lpbs-nn.epfl.ch/.
+
 ## Installation
 
 1. Clone this repository.
-
 2. Download the weights of the neural network under the following link: https://drive.google.com/file/d/1UTivmx_aEMpeGdOkCZO1CS9mcdJ3zmw2/edit. Put it in the folder `/unet`.
-
 3. Make sure that you have python 3, as well as all packages listed in the packages.txt folder, installed. Note that Tensorflow v2 is supported, but as the program was developed with Tensorflow v1.9.0, we cannot promise that it will run bug free. Also, it is imperative to install openpyxl 3.0.1, as a bug in openpyxl 3.0.2 prevents saving to an excel file.
-
 4. Run the program from your command line with `python GUI_main.py`
 
-   
-
-    
+## User Guide