Merge pull request #9756 from pranitbauva1997:doc-typo-faster

doc: fix typo in py_tutorials

doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown

@@ -130,7 +130,7 @@ Or
 >>> b = img[:,:,0]
 @endcode
 Suppose, you want to make all the red pixels to zero, you need not split like this and put it equal
-to zero. You can simply use Numpy indexing, and that is more faster.
+to zero. You can simply use Numpy indexing, and that is faster.
 @code{.py}
 >>> img[:,:,2] = 0
 @endcode

doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown

@@ -140,7 +140,7 @@ FLANN based Matcher
 
 FLANN stands for Fast Library for Approximate Nearest Neighbors. It contains a collection of
 algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional
-features. It works more faster than BFMatcher for large datasets. We will see the second example
+features. It works faster than BFMatcher for large datasets. We will see the second example
 with FLANN based matcher.
 
 For FLANN based matcher, we need to pass two dictionaries which specifies the algorithm to be used,

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -34,7 +34,7 @@ applications, rotation invariance is not required, so no need of finding this or
 speeds up the process. SURF provides such a functionality called Upright-SURF or U-SURF. It improves
 speed and is robust upto \f$\pm 15^{\circ}\f$. OpenCV supports both, depending upon the flag,
 **upright**. If it is 0, orientation is calculated. If it is 1, orientation is not calculated and it
-is more faster.
+is faster.
 
 ![image](images/surf_orientation.jpg)
 
@@ -130,7 +130,7 @@ False
 
 >>> plt.imshow(img2),plt.show()
 @endcode
-See the results below. All the orientations are shown in same direction. It is more faster than
+See the results below. All the orientations are shown in same direction. It is faster than
 previous. If you are working on cases where orientation is not a problem (like panorama stitching)
 etc, this is better.
 

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown

@@ -99,7 +99,7 @@ as 0-0.99, 1-1.99, 2-2.99 etc. So final range would be 255-255.99. To represent
 np.histogram(). So for one-dimensional histograms, you can better try that. Don't forget to set
 minlength = 256 in np.bincount. For example, hist = np.bincount(img.ravel(),minlength=256)
 
-@note OpenCV function is more faster than (around 40X) than np.histogram(). So stick with OpenCV
+@note OpenCV function is faster than (around 40X) than np.histogram(). So stick with OpenCV
 function.
 
 Now we should plot histograms, but how?