Merge pull request #25042 from mshabunin:doc-upgrade

Documentation transition to fresh Doxygen #25042

* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638

doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown

@@ -22,7 +22,7 @@ In this tutorial, we first introduce how to obtain the custom OCR model, then ho
 
 After completing the model training, please use [transform_to_onnx.py](https://github.com/zihaomu/deep-text-recognition-benchmark/blob/master/transform_to_onnx.py) to convert the model into onnx format.
 
-#### Execute in webcam
+### Execute in webcam
 The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.py).
 
 Example:
@@ -44,9 +44,10 @@ Their performance at different text recognition datasets is shown in the table b
 | CRNN_VGG-BiLSTM-CTC | 82.63    | 82.07 | 92.96     | 88.867     | 66.28     | 71.01  | 62.37    | 78.03       | 8.45              |
 | ResNet-CTC           | 84.00        | 84.08  | 92.39     | 88.96     | 67.74     | 74.73  | 67.60    | 79.93    | 44.28             |
 
-The performance of the text recognition model were tesred on OpenCV DNN, and does not include the text detection model.
+The performance of the text recognition model were tested on OpenCV DNN, and does not include the text detection model.
+
+### Model selection suggestion
 
-#### Model selection suggestion:
 The input of text recognition model is the output of the text detection model, which causes the performance of text detection to greatly affect the performance of text recognition.
 
 DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

@@ -17,7 +17,7 @@ In this tutorial you will learn how to use opencv_dnn module for image classific
 GoogLeNet trained network from [Caffe model zoo](http://caffe.berkeleyvision.org/model_zoo.html).
 
 We will demonstrate results of this example on the following picture.
-![Buran space shuttle](images/space_shuttle.jpg)
+![Buran space shuttle](dnn/images/space_shuttle.jpg)
 
 Source Code
 -----------

doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown

@@ -14,7 +14,7 @@
 In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail.
 
 ---
-#### TextRecognitionModel:
+### TextRecognitionModel
 
 In the current version, @ref cv::dnn::TextRecognitionModel only supports CNN+RNN+CTC based algorithms,
 and the greedy decoding method for CTC is provided.
@@ -38,7 +38,7 @@ Before recognition, you should `setVocabulary` and `setDecodeType`.
 
 ---
 
-#### TextDetectionModel:
+### TextDetectionModel
 
 @ref cv::dnn::TextDetectionModel API provides these methods for text detection:
 - cv::dnn::TextDetectionModel::detect() returns the results in std::vector<std::vector<Point>> (4-points quadrangles)
@@ -60,7 +60,7 @@ We encourage you to add new algorithms to these APIs.
 
 ## Pretrained Models
 
-#### TextRecognitionModel:
+### TextRecognitionModel
 
 ```
 crnn.onnx:
@@ -92,7 +92,7 @@ More models can be found in [here](https://drive.google.com/drive/folders/1cTbQ3
 which are taken from [clovaai](https://github.com/clovaai/deep-text-recognition-benchmark).
 You can train more models by [CRNN](https://github.com/meijieru/crnn.pytorch), and convert models by `torch.onnx.export`.
 
-#### TextDetectionModel:
+### TextDetectionModel
 
 ```
 - DB_IC15_resnet50.onnx:
@@ -297,7 +297,7 @@ For more information, please refer to:
 - [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.cpp)
 - [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/4.x/samples/dnn/scene_text_spotting.cpp)
 
-#### Test with an image
+### Test with an image
 Examples:
 ```bash
 example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=/path/to/alphabet_94.txt
@@ -306,7 +306,7 @@ example_dnn_scene_text_spotting -dmp=path/to/DB_IC15_resnet50.onnx -rmp=path/to/
 example_dnn_text_detection -dmp=path/to/EAST.pb -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=path/to/alphabet_94.txt
 ```
 
-#### Test on public datasets
+### Test on public datasets
 Text Recognition:
 
 The download link for testing images can be found in the **Images for Testing**