Merge pull request #21743 from luzpaz:typos

doc/js_tutorials/js_assets/webnn-electron/package.json

@@ -1,7 +1,7 @@
 {
   "name": "image_classification",
   "version": "0.0.1",
-  "description": "An Electon.js example of image_classification using webnn-native",
+  "description": "An Electron.js example of image_classification using webnn-native",
   "main": "main.js",
   "author": "WebNN-native Authors",
   "license": "Apache-2.0",

doc/js_tutorials/js_setup/js_setup/js_setup.markdown

@@ -97,10 +97,10 @@ Building OpenCV.js from Source
     @endcode
 
     @note
-    The loader is implemented as a js file in the path `<opencv_js_dir>/bin/loader.js`. The loader utilizes the [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) to detect the features of the broswer and load corresponding OpenCV.js automatically. To use it, you need to use the UMD version of [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) and introduce the `loader.js` in your Web application.
+    The loader is implemented as a js file in the path `<opencv_js_dir>/bin/loader.js`. The loader utilizes the [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) to detect the features of the browser and load corresponding OpenCV.js automatically. To use it, you need to use the UMD version of [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) and introduce the `loader.js` in your Web application.
 
     Example Code:
-    @code{.javascipt}
+    @code{.javascript}
     // Set paths configuration
     let pathsConfig = {
         wasm: "../../build_wasm/opencv.js",
@@ -173,7 +173,7 @@ This snippet and the following require [Node.js](https://nodejs.org) to be insta
 
 ### Headless with Puppeteer
 
-Alternatively tests can run with [GoogleChrome/puppeteer](https://github.com/GoogleChrome/puppeteer#readme) which is a version of Google Chrome that runs in the terminal (useful for Continuos integration like travis CI, etc)
+Alternatively tests can run with [GoogleChrome/puppeteer](https://github.com/GoogleChrome/puppeteer#readme) which is a version of Google Chrome that runs in the terminal (useful for Continuous integration like travis CI, etc)
 
 @code{.sh}
 cd build_js/bin
@@ -229,7 +229,7 @@ node tests.js
     The simd optimization is experimental as wasm simd is still in development.
 
     @note
-    Now only emscripten LLVM upstream backend supports wasm simd, refering to https://emscripten.org/docs/porting/simd.html. So you need to setup upstream backend environment with the following command first:
+    Now only emscripten LLVM upstream backend supports wasm simd, referring to https://emscripten.org/docs/porting/simd.html. So you need to setup upstream backend environment with the following command first:
     @code{.bash}
     ./emsdk update
     ./emsdk install latest-upstream

doc/tutorials/calib3d/usac.markdown

@@ -244,9 +244,9 @@ Samples:
 There are three new sample files in opencv/samples directory.
 
 1.  `epipolar_lines.cpp` – input arguments of `main` function are two
-    pathes to images. Then correspondences are found using
+    paths to images. Then correspondences are found using
     SIFT detector. Fundamental matrix is found using RANSAC from
-    tentaive correspondences and epipolar lines are plot.
+    tentative correspondences and epipolar lines are plot.
 
 2.  `essential_mat_reconstr.cpp` – input arguments are path to data file
     containing image names and single intrinsic matrix and directory

doc/tutorials/core/how_to_use_OpenCV_parallel_for_new/how_to_use_OpenCV_parallel_for_new.markdown

@@ -92,7 +92,7 @@ We then fill value to the corresponding pixel in the dst image.
 
 ### Parallel implementation
 
-When looking at the sequential implementation, we can notice that each pixel depends on multiple neighbouring pixels but only one pixel is edited at a time. Thus, to optimize the computation, we can split the image into stripes and parallely perform convolution on each, by exploiting the multi-core architecture of modern processor. The OpenCV @ref cv::parallel_for_ framework automatically decides how to split the computation efficiently and does most of the work for us.
+When looking at the sequential implementation, we can notice that each pixel depends on multiple neighbouring pixels but only one pixel is edited at a time. Thus, to optimize the computation, we can split the image into stripes and parallelly perform convolution on each, by exploiting the multi-core architecture of modern processor. The OpenCV @ref cv::parallel_for_ framework automatically decides how to split the computation efficiently and does most of the work for us.
 
 @note Although values of a pixel in a particular stripe may depend on pixel values outside the stripe, these are only read only operations and hence will not cause undefined behaviour.
 

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`

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -142,7 +142,7 @@ being a Graph API, doesn't force its users to do that.
 However, a graph is still built implicitly when a cv::GComputation
 object is defined. It may be useful to inspect how the resulting graph
 looks like to check if it is generated correctly and if it really
-represents our alrogithm. It is also useful to learn the structure of
+represents our algorithm. It is also useful to learn the structure of
 the graph to see if it has any redundancies.
 
 G-API allows to dump generated graphs to `.dot` files which then

doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown

@@ -241,7 +241,7 @@ pipeline is compiled for streaming:
 cv::GComputation::compileStreaming() triggers a special video-oriented
 form of graph compilation where G-API is trying to optimize
 throughput. Result of this compilation is an object of special type
-cv::GStreamingCompiled -- in constract to a traditional callable
+cv::GStreamingCompiled -- in contrast to a traditional callable
 cv::GCompiled, these objects are closer to media players in their
 semantics.
 

doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown

@@ -79,7 +79,7 @@ The main function is rather simple, as follows from the comments we do the follo
    In general callback functions are used to react to some kind of signal, in our
    case it's trackbar's state change.
    Explicit one-time call of `thresh_callback` is necessary to display
-   the "Contours" window simultaniously with the "Source" window.
+   the "Contours" window simultaneously with the "Source" window.
 
 @add_toggle_cpp
 @snippet samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp trackbar

doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown

@@ -240,7 +240,7 @@ taken:
 Hello OpenCV Sample
 -------------------
 
-Here are basic steps to guide you trough the process of creating a simple OpenCV-centric
+Here are basic steps to guide you through the process of creating a simple OpenCV-centric
 application. It will be capable of accessing camera output, processing it and displaying the result.
 
 -#  Open Eclipse IDE, create a new clean workspace, create a new Android project

doc/tutorials/introduction/linux_gdb_pretty_printer/linux_gdb_pretty_printer.markdown

@@ -20,7 +20,7 @@ This pretty-printer can show element type, `is_continuous`, `is_submatrix` flags
 
 # Installation {#tutorial_linux_gdb_pretty_printer_installation}
 
-Move into `opencv/samples/gdb/`. Place `mat_pretty_printer.py` in a convinient place, rename `gdbinit` to `.gdbinit`  and move it into your home folder. Change 'source' line of `.gdbinit` to point to your `mat_pretty_printer.py` path.
+Move into `opencv/samples/gdb/`. Place `mat_pretty_printer.py` in a convenient place, rename `gdbinit` to `.gdbinit`  and move it into your home folder. Change 'source' line of `.gdbinit` to point to your `mat_pretty_printer.py` path.
 
 In order to check version of python bundled with your gdb, use the following commands from the gdb shell:
 
@@ -34,5 +34,5 @@ If the version of python 3 installed in your system doesn't match the version in
 
 # Usage {#tutorial_linux_gdb_pretty_printer_usage}
 
-The fields in a debugger prefixed with `view_` are pseudo-fields added for convinience, the rest are left as is.
-If you feel that the number of elements in truncated view is too low, you can edit `mat_pretty_printer.py` - `np.set_printoptions` controlls everything matrix display-related.
+The fields in a debugger prefixed with `view_` are pseudo-fields added for convenience, the rest are left as is.
+If you feel that the number of elements in truncated view is too low, you can edit `mat_pretty_printer.py` - `np.set_printoptions` controls everything matrix display-related.

doc/tutorials/ios/image_manipulation/image_manipulation.markdown

@@ -22,7 +22,7 @@ Introduction
 In *OpenCV* all the image processing operations are usually carried out on the *Mat* structure. In
 iOS however, to render an image on screen it have to be an instance of the *UIImage* class. To
 convert an *OpenCV Mat* to an *UIImage* we use the *Core Graphics* framework available in iOS. Below
-is the code needed to covert back and forth between Mat's and UIImage's.
+is the code needed to convert back and forth between Mat's and UIImage's.
 @code{.m}
 - (cv::Mat)cvMatFromUIImage:(UIImage *)image
 {