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

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* Fixed freezing tf1 pre-trained model issue due to mix use of tf1 and tf2 API
* Fix review comments
* Apply suggestions from code review
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docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md
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# 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
```