Merge branch 4.x

doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown

@@ -70,7 +70,7 @@ Sometimes networks built using blocked structure that means some layer are
 identical or quite similar. If you want to apply the same scheduling for
 different layers accurate to tiling or vectorization factors, define scheduling
 patterns in section `patterns` at the beginning of scheduling file.
-Also, your patters may use some parametric variables.
+Also, your patterns may use some parametric variables.
 @code
 # At the beginning of the file
 patterns:

doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown

@@ -29,8 +29,8 @@ Before recognition, you should `setVocabulary` and `setDecodeType`.
 - "CTC-prefix-beam-search", the output of the text recognition model should be a probability matrix same with "CTC-greedy".
     - The algorithm is proposed at Hannun's [paper](https://arxiv.org/abs/1408.2873).
     - `setDecodeOptsCTCPrefixBeamSearch` could be used to control the beam size in search step.
-    - To futher optimize for big vocabulary, a new option `vocPruneSize` is introduced to avoid iterate the whole vocbulary
-       but only the number of `vocPruneSize` tokens with top probabilty.
+    - To further optimize for big vocabulary, a new option `vocPruneSize` is introduced to avoid iterate the whole vocbulary
+       but only the number of `vocPruneSize` tokens with top probability.
 
 @ref cv::dnn::TextRecognitionModel::recognize() is the main function for text recognition.
 - The input image should be a cropped text image or an image with `roiRects`