diff --git a/source/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.rst b/source/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.rst
index b1a6613..b29d071 100644
--- a/source/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.rst
+++ b/source/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.rst
@@ -23,7 +23,7 @@ Morphological transformations are some simple operations based on the image shap
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 The basic idea of erosion is just like soil erosion only, it erodes away the boundaries of foreground object (Always try to keep foreground in white). So what does it do? The kernel slides through the image (as in 2D convolution). A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded (made to zero). 
 
-So what happends is that, all the pixels near boundary will be discarded depending upon the size of kernel. So the thickness or size of the foreground object decreases or simply white region decreases in the image. It is useful for removing small white noises (as we have seen in colorspace chapter), detach two connected objects etc.
+So what happens is that, all the pixels near boundary will be discarded depending upon the size of kernel. So the thickness or size of the foreground object decreases or simply white region decreases in the image. It is useful for removing small white noises (as we have seen in colorspace chapter), detach two connected objects etc.
 
 Here, as an example, I would use a 5x5 kernel with full of ones. Let's see it how it works:
 ::