fix errors in documentation (#9384)

* add sphinx log parsing

* fix

* fix log

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* doxygen-xfail

* fixes

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* fix pot

* add pot check

* fixes

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* Fixed POT docs

* Fixed POT docs

* Fixes

* change heading markup

* fixes

Co-authored-by: azaytsev <andrey.zaytsev@intel.com>

samples/cpp/benchmark_app/README.md

@@ -5,11 +5,6 @@ Performance can be measured for two inference modes: latency- and throughput-ori
 
 > **NOTE**: This topic describes usage of C++ implementation of the Benchmark Tool. For the Python* implementation, refer to [Benchmark Python* Tool](../../../tools/benchmark_tool/README.md).
 
-> **TIP**: You can quick start with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
-> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI you to
-> import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for
-> deployment on various Intel® platforms.
-
 
 ## How It Works
 
@@ -19,7 +14,7 @@ Upon start-up, the application reads command-line parameters and loads a network
   If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application
   or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified.
   For more information about the argument, refer to **When to Reverse Input Channels** section of
-  [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+  [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 
 Device-specific execution parameters (number of streams, threads, and so on) can be either explicitly specified through the command line
 or left default. In the last case, the sample logic will select the values for the optimal throughput.
@@ -59,11 +54,11 @@ Note that the benchmark_app usually produces optimal performance for any device
 ./benchmark_app -m <model> -i <input> -d CPU
 ```
 
-But it is still may be sub-optimal for some cases, especially for very small networks. More details can read in [Introduction to Performance Topics](../../../docs/IE_DG/Intro_to_Performance.md).
+But it is still may be sub-optimal for some cases, especially for very small networks. More details can read in [Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md).
 
-As explained in the  [Introduction to Performance Topics](../../../docs/IE_DG/Intro_to_Performance.md) section, for all devices, including new [MULTI device](../../../docs/IE_DG/supported_plugins/MULTI.md) it is preferable to use the FP16 IR for the model.
+As explained in the  [Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md) section, for all devices, including new [MULTI device](../../../docs/IE_DG/supported_plugins/MULTI.md) it is preferable to use the FP16 IR for the model.
 Also if latency of the CPU inference on the multi-socket machines is of concern, please refer to the same
-[Introduction to Performance Topics](../../../docs/IE_DG/Intro_to_Performance.md) document.
+[Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md).
 
 Running the application with the `-h` option yields the following usage message:
 ```

samples/cpp/classification_sample_async/README.md

@@ -52,7 +52,7 @@ To run the sample, you need specify a model and image:
 
 > **NOTES**:
 >
-> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 >
 > - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >

samples/cpp/hello_classification/README.md

@@ -41,7 +41,7 @@ To run the sample, you need specify a model and image:
 
 > **NOTES**:
 >
-> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 >
 > - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >

samples/cpp/hello_nv12_input_classification/README.md

@@ -59,7 +59,7 @@ ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv
 >   model to work with RGB order, you need to reconvert your model using the Model Optimizer tool
 >   with `--reverse_input_channels` argument specified. For more information about the argument,
 >   refer to **When to Reverse Input Channels** section of
->   [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+>   [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 > - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
 > - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

samples/cpp/hello_reshape_ssd/README.md

@@ -41,7 +41,7 @@ To run the sample, you need specify a model and image:
 
 > **NOTES**:
 >
-> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
 >
 > - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >