Merge pull request #16001 from alalek:backport_15995

doc/js_tutorials/js_gui/js_trackbar/js_trackbar.markdown

@@ -36,7 +36,7 @@ let x = document.getElementById('myRange');
 @endcode
 
 As a trackbar, the range element need a trackbar name, the default value, minimum value, maximum value,
-step and the callback function which is executed everytime trackbar value changes. The callback function
+step and the callback function which is executed every time trackbar value changes. The callback function
 always has a default argument, which is the trackbar position. Additionally, a text element to display the
 trackbar value is fine. In our case, we can create the trackbar as below:
 @code{.html}

doc/py_tutorials/py_gui/py_trackbar/py_trackbar.markdown

@@ -16,7 +16,7 @@ correspondingly window color changes. By default, initial color will be set to B
 
 For cv.getTrackbarPos() function, first argument is the trackbar name, second one is the window
 name to which it is attached, third argument is the default value, fourth one is the maximum value
-and fifth one is the callback function which is executed everytime trackbar value changes. The
+and fifth one is the callback function which is executed every time trackbar value changes. The
 callback function always has a default argument which is the trackbar position. In our case,
 function does nothing, so we simply pass.
 

doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown

@@ -54,7 +54,7 @@ print( accuracy )
 @endcode
 So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option
 improve accuracy is to add more data for training, especially the wrong ones. So instead of finding
-this training data everytime I start application, I better save it, so that next time, I directly
+this training data every time I start application, I better save it, so that next time, I directly
 read this data from a file and start classification. You can do it with the help of some Numpy
 functions like np.savetxt, np.savez, np.load etc. Please check their docs for more details.
 @code{.py}

doc/tutorials/imgproc/random_generator_and_text/random_generator_and_text.markdown

@@ -210,12 +210,12 @@ Explanation
     @code{.cpp}
     image2 = image - Scalar::all(i)
     @endcode
-    So, **image2** is the substraction of **image** and **Scalar::all(i)**. In fact, what happens
-    here is that every pixel of **image2** will be the result of substracting every pixel of
+    So, **image2** is the subtraction of **image** and **Scalar::all(i)**. In fact, what happens
+    here is that every pixel of **image2** will be the result of subtracting every pixel of
     **image** minus the value of **i** (remember that for each pixel we are considering three values
     such as R, G and B, so each of them will be affected)
 
-    Also remember that the substraction operation *always* performs internally a **saturate**
+    Also remember that the subtraction operation *always* performs internally a **saturate**
     operation, which means that the result obtained will always be inside the allowed range (no
     negative and between 0 and 255 for our example).
 

modules/core/include/opencv2/core/hal/msa_macros.h

@@ -502,7 +502,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
   (v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__e, 31), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \
 })
 
-/* Minimum values between corresponding elements in the two vectors are written to teh returned vector. */
+/* Minimum values between corresponding elements in the two vectors are written to the returned vector. */
 #define msa_minq_s8(__a, __b)  (__builtin_msa_min_s_b(__a, __b))
 #define msa_minq_s16(__a, __b) (__builtin_msa_min_s_h(__a, __b))
 #define msa_minq_s32(__a, __b) (__builtin_msa_min_s_w(__a, __b))
@@ -514,7 +514,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
 #define msa_minq_f32(__a, __b) (__builtin_msa_fmin_w(__a, __b))
 #define msa_minq_f64(__a, __b) (__builtin_msa_fmin_d(__a, __b))
 
-/* Maximum values between corresponding elements in the two vectors are written to teh returned vector. */
+/* Maximum values between corresponding elements in the two vectors are written to the returned vector. */
 #define msa_maxq_s8(__a, __b)  (__builtin_msa_max_s_b(__a, __b))
 #define msa_maxq_s16(__a, __b) (__builtin_msa_max_s_h(__a, __b))
 #define msa_maxq_s32(__a, __b) (__builtin_msa_max_s_w(__a, __b))

modules/imgproc/test/test_watershed.cpp

@@ -82,7 +82,7 @@ void CV_WatershedTest::run( int /* start_from */)
         Point* p = (Point*)cvGetSeqElem(cnts, 0);
 
         //expected image was added with 1 in order to save to png
-        //so now we substract 1 to get real color
+        //so now we subtract 1 to get real color
         if(!exp.empty())
             colors.push_back(exp.ptr(p->y)[p->x] - 1);
     }