Fix some typos in doc.

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -18,7 +18,7 @@ is sufficient to find the object exactly on the trainImage.
 
 For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
 of points from both the images, it will find the perspective transformation of that object. Then we
-can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
+can use **cv.perspectiveTransform()** to find the object. It needs at least four correct points to
 find the transformation.
 
 We have seen that there can be some possible errors while matching which may affect the result. To
@@ -64,7 +64,7 @@ for m,n in matches:
     if m.distance < 0.7*n.distance:
         good.append(m)
 @endcode
-Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are to be there to
+Now we set a condition that at least 10 matches (defined by MIN_MATCH_COUNT) are to be there to
 find the object. Otherwise simply show a message saying not enough matches are present.
 
 If enough matches are found, we extract the locations of matched keypoints in both the images. They

doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown

@@ -48,7 +48,7 @@ Result:
 
 ### 2. Dilation
 
-It is just opposite of erosion. Here, a pixel element is '1' if atleast one pixel under the kernel
+It is just opposite of erosion. Here, a pixel element is '1' if at least one pixel under the kernel
 is '1'. So it increases the white region in the image or size of foreground object increases.
 Normally, in cases like noise removal, erosion is followed by dilation. Because, erosion removes
 white noises, but it also shrinks our object. So we dilate it. Since noise is gone, they won't come