diff --git a/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown b/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
index e4ee61ebd2..de9eaaf8c6 100644
--- a/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
+++ b/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
@@ -51,23 +51,6 @@ You can modify the pixel values the same way.
 Numpy is an optimized library for fast array calculations. So simply accessing each and every pixel
 value and modifying it will be very slow and it is discouraged.
 
-@note The above method is normally used for selecting a region of an array, say the first 5 rows
-and last 3 columns. For individual pixel access, the Numpy array methods, array.item() and
-array.itemset() are considered better. They always return a scalar, however, so if you want to access
-all the B,G,R values, you will need to call array.item() separately for each value.
-
-Better pixel accessing and editing method :
-@code{.py}
-# accessing RED value
->>> img.item(10,10,2)
-59
-
-# modifying RED value
->>> img.itemset((10,10,2),100)
->>> img.item(10,10,2)
-100
-@endcode
-
 Accessing Image Properties
 --------------------------