DOCS: Fixing formatting issues and broken links - port to master (#13143)

Porting:
https://github.com/openvinotoolkit/openvino/pull/13101
https://github.com/openvinotoolkit/openvino/pull/13103
https://github.com/openvinotoolkit/openvino/pull/13104
https://github.com/openvinotoolkit/openvino/pull/13106
https://github.com/openvinotoolkit/openvino/pull/13107
https://github.com/openvinotoolkit/openvino/pull/13109

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>

docs/MO_DG/prepare_model/Getting_performance_numbers.md

@@ -9,7 +9,7 @@ When evaluating the performance of a model with OpenVINO Runtime, it is required
 
 - Track operations that occur outside OpenVINO Runtime (such as video decoding) separately. 
 
-> **NOTE**: Some image pre-processing can be baked into OpenVINO IR and accelerated accordingly. For more information, refer to [Embedding the Pre-processing](Additional_Optimizations.md) and [General Runtime Optimizations](../../optimization_guide/dldt_deployment_optimization_common).
+> **NOTE**: Some image pre-processing can be baked into OpenVINO IR and accelerated accordingly. For more information, refer to [Embedding the Pre-processing](Additional_Optimizations.md) and [General Runtime Optimizations](../../optimization_guide/dldt_deployment_optimization_common.md).
 
 ## Tip 2: Try to Get Credible Data
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md

@@ -39,7 +39,7 @@ The following PaddlePaddle models have been officially validated and confirmed t
      - Models are exported from `PaddleClas <https://github.com/PaddlePaddle/PaddleClas/tree/release/2.1/>`_. Refer to `getting_started_en.md <https://github.com/PaddlePaddle/PaddleClas/blob/release/2.1/docs/en/tutorials/getting_started_en.md#4-use-the-inference-model-to-predict>`_.
    * - MobileNet v3
      - classification
-     - Models are exported from `PaddleClas <https://github.com/PaddlePaddle/PaddleClas/tree/release/2.1/)>`_. Refer to `getting_started_en.md <https://github.com/PaddlePaddle/PaddleClas/blob/release/2.1/docs/en/tutorials/getting_started_en.md#4-use-the-inference-model-to-predict>`_.
+     - Models are exported from `PaddleClas <https://github.com/PaddlePaddle/PaddleClas/tree/release/2.1/>`_. Refer to `getting_started_en.md <https://github.com/PaddlePaddle/PaddleClas/blob/release/2.1/docs/en/tutorials/getting_started_en.md#4-use-the-inference-model-to-predict>`_.
    * - BiSeNet v2
      - semantic segmentation
      - Models are exported from `PaddleSeg <https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.1>`_. Refer to `model_export.md <https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.1/docs/model_export.md#>`_.

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md

@@ -103,6 +103,4 @@ mo --input_model rnnt_encoder.onnx --input "input[157 1 240],feature_length->157
 mo --input_model rnnt_prediction.onnx --input "symbol[1 1],hidden_in_1[2 1 320],hidden_in_2[2 1 320]"
 mo --input_model rnnt_joint.onnx --input "0[1 1 1024],1[1 1 320]"
 ```
-> **NOTE**: The hardcoded value for sequence length = 157 was taken from the MLCommons, but conversion to IR preserves
-network [reshapeability](../../../../OV_Runtime_UG/ShapeInference.md). Therefore, input shapes can be changed manually to any value during either conversion or
-inference.
+> **NOTE**: The hardcoded value for sequence length = 157 was taken from the MLCommons, but conversion to IR preserves network [reshapeability](../../../../OV_Runtime_UG/ShapeInference.md). Therefore, input shapes can be changed manually to any value during either conversion or inference.

docs/OV_Runtime_UG/ov_infer_request.md

@@ -222,6 +222,7 @@ You can use the `ov::InferRequest::cancel` method if you want to abort execution
 
     @endsphinxtabset
 
+
 ## Examples of Infer Request Usages
 
 Presented below are examples of what the Infer Request can be used for.

tools/pot/openvino/tools/pot/algorithms/quantization/range_supervision/README.md

@@ -10,9 +10,9 @@ where $T_{low}$ and $T_{up}$ are the lower and upper bounds for the particular p
 
 
 
-The process flow follows the diagram [Fig 1](#Schematic). Starting from the internal representation (IR) of an OpenVINO model, the POT RangeSupervision algorithm is called to **add protection layers into the model graph**. This step requires **appropriate threshold values that are automatically extracted from a specified test dataset**. The result is an IR representation of the model with additional "RangeSupervision" layers after each supported activation layer. The original and the modified model can be called in the same way through the OpenVINO inference engine to evaluate the impact on accuracy, performance, and dependability in the presence of potential soft errors (for example using the *benchmark_app* and *accuracy_checker* functions). **The algorithm is designed to provide efficient protection at negligible performance overhead or accuracy impact in the absence of faults.** Bound extraction is a one-time effort and the protected IR model returned by the RangeSupervision algorithm can be used independently from there on. No changes in the learned parameters of the network are needed.
-
+The process flow follows the diagram [Fig 1](@ref schematic). Starting from the internal representation (IR) of an OpenVINO model, the POT RangeSupervision algorithm is called to **add protection layers into the model graph**. This step requires **appropriate threshold values that are automatically extracted from a specified test dataset**. The result is an IR representation of the model with additional "RangeSupervision" layers after each supported activation layer. The original and the modified model can be called in the same way through the OpenVINO inference engine to evaluate the impact on accuracy, performance, and dependability in the presence of potential soft errors (for example using the *benchmark_app* and *accuracy_checker* functions). **The algorithm is designed to provide efficient protection at negligible performance overhead or accuracy impact in the absence of faults.** Bound extraction is a one-time effort and the protected IR model returned by the RangeSupervision algorithm can be used independently from there on. No changes in the learned parameters of the network are needed.
 
+@anchor schematic
 ![Schematic](../../../../../../docs/range_supervision/images/scheme3.png)
 
 *Fig 1: Schematic of RangeSupervision process flow.*

tools/pot/openvino/tools/pot/api/samples/object_detection/README.md

@@ -2,7 +2,7 @@
 
 This example demonstrates the use of the [Post-training Optimization Toolkit API](@ref pot_compression_api_README) to
  quantize an object detection model in the [accuracy-aware mode](@ref accuracy_aware_README).
-The [MobileNetV1 FPN](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/ssd_mobilenet_v1_fpn_coco/ssd_mobilenet_v1_fpn_coco.md) model from TensorFlow* for object detection task is used for this purpose.
+The [MobileNetV1 FPN](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_fpn_coco) model from TensorFlow for object detection task is used for this purpose.
 A custom `DataLoader` is created to load the [COCO](https://cocodataset.org/) dataset for object detection task 
 and the implementation of mAP COCO is used for the model evaluation. The code of the example is available on [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/object_detection).