Docs/fix convert tf crnn model document (openvinotoolkit#14531)

* Fixed freezing tf1 pre-trained model issue due to mix use of tf1 and tf2 API
* Fix review comments
* Apply suggestions from code review

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md

@@ -1,56 +1,52 @@
 # Converting a TensorFlow CRNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_CRNN_From_Tensorflow}
 
-This tutorial explains how to convert a CRNN model to Intermediate Representation (IR).
+This tutorial explains how to convert a CRNN model to OpenVINO™ Intermediate Representation (IR).
 
 There are several public versions of TensorFlow CRNN model implementation available on GitHub. This tutorial explains how to convert the model from
-the [CRNN Tensorflow](https://github.com/MaybeShewill-CV/CRNN_Tensorflow) repository to IR. 
+the [CRNN Tensorflow](https://github.com/MaybeShewill-CV/CRNN_Tensorflow) repository to IR, and is validated with Python 3.7, TensorFlow 1.15.0, and protobuf 3.19.0.
 If you have another implementation of CRNN model, it can be converted to OpenVINO IR in a similar way. You need to get inference graph and run Model Optimizer on it.
 
-**To convert this model to the IR:**
+**To convert the model to IR:**
 
-**Step 1.** Clone this GitHub repository and checkout the commit:
-    1. Clone repository:
+**Step 1.** Clone this GitHub repository and check out the commit:
+    1. Clone the repository:
 ```sh
- git clone https://github.com/MaybeShewill-CV/CRNN_Tensorflow.git
+git clone https://github.com/MaybeShewill-CV/CRNN_Tensorflow.git
 ```
-    2. Checkout necessary commit:
+    2. Go to the `CRNN_Tensorflow` directory of the cloned repository:
+```sh
+cd path/to/CRNN_Tensorflow
+```
+    3. Check out the necessary commit:
 ```sh
 git checkout 64f1f1867bffaacfeacc7a80eebf5834a5726122
 ```
 
-**Step 2.** Train the model, using framework or use the pretrained checkpoint provided in this repository.
+**Step 2.** Train the model using the framework or the pretrained checkpoint provided in this repository.
 
 **Step 3.** Create an inference graph:
-    1. Go to the `CRNN_Tensorflow` directory of the cloned repository:
-```sh
-cd path/to/CRNN_Tensorflow
-```
-    2. Add `CRNN_Tensorflow` folder to `PYTHONPATH`.
-       * For Linux OS:
+    1. Add the `CRNN_Tensorflow` folder to `PYTHONPATH`.
+       * For Linux:
 ```sh
 export PYTHONPATH="${PYTHONPATH}:/path/to/CRNN_Tensorflow/"
 ```
-       * For  Windows OS add `/path/to/CRNN_Tensorflow/` to the `PYTHONPATH` environment variable in settings.
-    3. Open the `tools/test_shadownet.py` script. After `saver.restore(sess=sess, save_path=weights_path)` line, add the following code:
+       * For  Windows, add `/path/to/CRNN_Tensorflow/` to the `PYTHONPATH` environment variable in settings.
+    2. Edit the `tools/demo_shadownet.py` script. After `saver.restore(sess=sess, save_path=weights_path)` line, add the following code:
 ```python
-import tensorflow as tf
 from tensorflow.python.framework import graph_io
-frozen = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['shadow/LSTMLayers/transpose_time_major'])
+frozen = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['shadow/LSTMLayers/transpose_time_major'])
 graph_io.write_graph(frozen, '.', 'frozen_graph.pb', as_text=False)
 ```
-    4. Run the demo with the following command:
+    3. Run the demo with the following command:
 ```sh
-python tools/test_shadownet.py --image_path data/test_images/test_01.jpg --weights_path model/shadownet/shadownet_2017-10-17-11-47-46.ckpt-199999
+python tools/demo_shadownet.py --image_path data/test_images/test_01.jpg --weights_path model/shadownet/shadownet_2017-10-17-11-47-46.ckpt-199999
 ```
    If you want to use your checkpoint, replace the path in the `--weights_path` parameter with a path to your checkpoint.
-    5. In the `CRNN_Tensorflow` directory, you will find the inference CRNN graph `frozen_graph.pb`. You can use this graph with the OpenVINO™ toolkit
-     to convert the model into the IR and run inference.
+    4. In the `CRNN_Tensorflow` directory, you will find the inference CRNN graph `frozen_graph.pb`. You can use this graph with OpenVINO 
+     to convert the model to IR and then run inference.
 
-**Step 4.** Convert the model into the IR:
+**Step 4.** Convert the model to IR:
 ```sh
 mo --input_model path/to/your/CRNN_Tensorflow/frozen_graph.pb
 ```
 
-
-
-