Fix spelling typos

backport commit 659ffaddb4

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -17,7 +17,7 @@ In short, we found locations of some parts of an object in another cluttered ima
 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 perpective transformation of that object. Then we
+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
 find the transformation.
 
@@ -68,7 +68,7 @@ Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are
 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
-are passed to find the perpective transformation. Once we get this 3x3 transformation matrix, we use
+are passed to find the perspective transformation. Once we get this 3x3 transformation matrix, we use
 it to transform the corners of queryImage to corresponding points in trainImage. Then we draw it.
 @code{.py}
 if len(good)>MIN_MATCH_COUNT:

doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown

@@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
 ![image](images/shitomasi_space.png)
 
 From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
-\f$\lambda_{min}\f$, it is conidered as a corner(green region).
+\f$\lambda_{min}\f$, it is considered as a corner(green region).
 
 Code
 ----

doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown

@@ -144,7 +144,7 @@ cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 ### 7.b. Rotated Rectangle
 
 Here, bounding rectangle is drawn with minimum area, so it considers the rotation also. The function
-used is **cv.minAreaRect()**. It returns a Box2D structure which contains following detals - (
+used is **cv.minAreaRect()**. It returns a Box2D structure which contains following details - (
 center (x,y), (width, height), angle of rotation ). But to draw this rectangle, we need 4 corners of
 the rectangle. It is obtained by the function **cv.boxPoints()**
 @code{.py}

doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown

@@ -185,7 +185,7 @@ array([[[ 3, -1,  1, -1],
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
 
-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.