Fix spelling typos

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -381,7 +381,7 @@ Here is explained in detail the code for the real time application:
     as not, there are false correspondences or also called *outliers*. The [Random Sample
     Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
     method which estimate parameters of a mathematical model from observed data producing an
-    approximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
+    approximate result as the number of iterations increase. After applying *Ransac* all the *outliers*
     will be eliminated to then estimate the camera pose with a certain probability to obtain a good
     solution.
 

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -153,7 +153,7 @@ file name before running the application, e.g.:
 
     $ GRAPH_DUMP_PATH=segm.dot ./bin/example_tutorial_porting_anisotropic_image_segmentation_gapi
 
-Now this file can be visalized with a `dot` command like this:
+Now this file can be visualized with a `dot` command like this:
 
     $ dot segm.dot -Tpng -o segm.png
 
@@ -368,7 +368,7 @@ visualization like this:
 
 ![Anisotropic image segmentation graph with OpenCV & Fluid kernels](pics/segm_fluid.gif)
 
-This graph doesn't differ structually from its previous version (in
+This graph doesn't differ structurally from its previous version (in
 terms of operations and data objects), though a changed layout (on the
 left side of the dump) is easily noticeable.
 

doc/tutorials/gapi/face_beautification/face_beautification.markdown

@@ -427,7 +427,7 @@ the ROI, which will lead to accuracy improvement.
 Unfortunately, another problem occurs if we do that:
 if the rectangular ROI is near the border, a describing square will probably go
 out of the frame --- that leads to errors of the landmarks detector.
-To aviod such a mistake, we have to implement an algorithm that, firstly,
+To avoid such a mistake, we have to implement an algorithm that, firstly,
 describes every rectangle by a square, then counts the farthest coordinates
 turned up to be outside of the frame and, finally, pads the source image by
 borders (e.g. single-colored) with the size counted. It will be safe to take

doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown

@@ -145,7 +145,7 @@ description requires three parameters:
    regular "functions" which take and return data. Here network
    `Faces` (a detector) takes a cv::GMat and returns a cv::GMat, while
    network `AgeGender` is known to provide two outputs (age and gender
-   blobs, respecitvely) -- so its has a `std::tuple<>` as a return
+   blobs, respectively) -- so its has a `std::tuple<>` as a return
    type.
 3. A topology name -- can be any non-empty string, G-API is using
    these names to distinguish networks inside. Names should be unique

doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown

@@ -499,7 +499,7 @@ using the following OpenCV methods:
 -   the imwrite static method from the Highgui class to write an image to a file
 -   the GaussianBlur static method from the Imgproc class to apply to blur the original image
 
-We're also going to use the Mat class which is returned from the imread method and accpeted as the
+We're also going to use the Mat class which is returned from the imread method and accepted as the
 main argument to both the GaussianBlur and the imwrite methods.
 
 ### Add an image to the project

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown

@@ -10,7 +10,7 @@ In this tutorial,
 -   We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
 -   We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
     will use the functions:
-    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
+    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifier
     -   @ref cv::CascadeClassifier::detectMultiScale to perform the detection.
 
 Theory

doc/tutorials/objdetect/traincascade.markdown

@@ -168,7 +168,7 @@ Command line arguments of opencv_traincascade application grouped by purposes:
   - `-w <sampleWidth>` :  Width of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
   - `-h <sampleHeight>` : Height of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
 
-- Boosted classifer parameters:
+- Boosted classifier parameters:
   - `-bt <{DAB, RAB, LB, GAB(default)}>` : Type of boosted classifiers: DAB - Discrete AdaBoost, RAB - Real AdaBoost, LB - LogitBoost, GAB - Gentle AdaBoost.
   - `-minHitRate <min_hit_rate>` : Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
   - `-maxFalseAlarmRate <max_false_alarm_rate>` : Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.

doc/tutorials/videoio/kinect_openni.markdown

@@ -43,7 +43,7 @@ VideoCapture can retrieve the following data:
     -   CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
     -   CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
     -   CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
-    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.)
+    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded etc.)
         (CV_8UC1)
 
 -#  data given from BGR image generator: