# Training documentation

This document aims to assist users in preparing training datasets, training models, and
performing inference with trained models.  We cover various use cases including
pixel-wise segmentation, image classification, image enhancement, and
machine-based reading order detection. For each use case, we provide guidance
on how to generate the corresponding training dataset.

The following three tasks can all be accomplished using the code in the
[`train`](https://github.com/qurator-spk/eynollah/tree/main/train) directory:

* generate training dataset
* train a model
* inference with the trained model

## Training, evaluation and output 

The train and evaluation folders should contain subfolders of `images` and `labels`.

The output folder should be an empty folder where the output model will be written to.

## Generate training dataset

The script `generate_gt_for_training.py` is used for generating training datasets. As the results of the following
command demonstrates, the dataset generator provides several subcommands:

```sh
eynollah-training generate-gt --help
```

The three most important subcommands are:

* image-enhancement
* machine-based-reading-order
* pagexml2label

### image-enhancement

Generating a training dataset for image enhancement is quite straightforward. All that is needed is a set of
high-resolution images. The training dataset can then be generated using the following command:

```sh
eynollah-training image-enhancement \
  -dis "dir of high resolution images" \
  -dois "dir where degraded images will be written" \
  -dols "dir where the corresponding high resolution image will be written as label" \
  -scs "degrading scales json file"
```

The scales JSON file is a dictionary with a key named `scales` and values representing scales smaller than 1. Images are
downscaled based on these scales and then upscaled again to their original size. This process causes the images to lose
resolution at different scales. The degraded images are used as input images, and the original high-resolution images
serve as labels. The enhancement model can be trained with this generated dataset. The scales JSON file looks like this:

```yaml
{
    "scales": [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]
}
```

### machine-based-reading-order

For machine-based reading order, we aim to determine the reading priority between two sets of text regions. The model's
input is a three-channel image: the first and last channels contain information about each of the two text regions,
while the middle channel encodes prominent layout elements necessary for reading order, such as separators and headers.
To generate the training dataset, our script requires a page XML file that specifies the image layout with the correct
reading order.

For output images, it is necessary to specify the width and height. Additionally, a minimum text region size can be set
to filter out regions smaller than this minimum size. This minimum size is defined as the ratio of the text region area
to the image area, with a default value of zero. To run the dataset generator, use the following command:

```shell
eynollah-training generate-gt machine-based-reading-order \
  -dx "dir of GT xml files" \
  -domi "dir where output images will be written" \
"" -docl "dir where the labels will be written" \
  -ih "height" \
  -iw "width" \
  -min "min area ratio"
```

### pagexml2label

pagexml2label is designed to generate labels from GT page XML files for various pixel-wise segmentation use cases,
including 'layout,' 'textline,' 'printspace,' 'glyph,' and 'word' segmentation.
To train a pixel-wise segmentation model, we require images along with their corresponding labels. Our training script
expects a PNG image where each pixel corresponds to a label, represented by an integer. The background is always labeled
as zero, while other elements are assigned different integers. For instance, if we have ground truth data with four
elements including the background, the classes would be labeled as 0, 1, 2, and 3 respectively.

In binary segmentation scenarios such as textline or page extraction, the background is encoded as 0, and the desired
element is automatically encoded as 1 in the PNG label.

To specify the desired use case and the elements to be extracted in the PNG labels, a custom JSON file can be passed.
For example, in the case of 'textline' detection, the JSON file would resemble this:

```yaml
{
"use_case": "textline"
}
```

In the case of layout segmentation a custom config json file can look like this:

```yaml
{
"use_case": "layout",
"textregions":{"rest_as_paragraph":1 , "drop-capital": 1, "header":2, "heading":2, "marginalia":3},
"imageregion":4,
"separatorregion":5,
"graphicregions" :{"rest_as_decoration":6 ,"stamp":7}
}
```

A possible custom config json file for layout segmentation where the "printspace" is a class:

```yaml
{
"use_case": "layout",
"textregions":{"rest_as_paragraph":1 , "drop-capital": 1, "header":2, "heading":2, "marginalia":3},
"imageregion":4,
"separatorregion":5,
"graphicregions" :{"rest_as_decoration":6 ,"stamp":7}
"printspace_as_class_in_layout" : 8
}
```

For the layout use case, it is beneficial to first understand the structure of the page XML file and its elements.
In a given image, the annotations of elements are recorded in a page XML file, including their contours and classes.
For an image document, the known regions are 'textregion', 'separatorregion', 'imageregion', 'graphicregion',
'noiseregion', and 'tableregion'.

Text regions and graphic regions also have their own specific types. The known types for text regions are 'paragraph',
'header', 'heading', 'marginalia', 'drop-capital', 'footnote', 'footnote-continued', 'signature-mark', 'page-number',
and 'catch-word'. The known types for graphic regions are 'handwritten-annotation', 'decoration', 'stamp', and
'signature'.
Since we don't know all types of text and graphic regions, unknown cases can arise. To handle these, we have defined
two additional types, "rest_as_paragraph" and "rest_as_decoration", to ensure that no unknown types are missed.
This way, users can extract all known types from the labels and be confident that no unknown types are overlooked.

In the custom JSON file shown above, "header" and "heading" are extracted as the same class, while "marginalia" is shown
as a different class. All other text region types, including "drop-capital," are grouped into the same class. For the
graphic region, "stamp" has its own class, while all other types are classified together. "Image region" and "separator
region" are also present in the label. However, other regions like "noise region" and "table region" will not be
included in the label PNG file, even if they have information in the page XML files, as we chose not to include them.

```sh
eynollah-training generate-gt pagexml2label \
  -dx "dir of GT xml files" \
  -do "dir where output label png files will be written" \
  -cfg "custom config json file" \
  -to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels"
```

We have also defined an artificial class that can be added to the boundary of text region types or text lines. This key
is called "artificial_class_on_boundary." If users want to apply this to certain text regions in the layout use case,
the example JSON config file should look like this:

```yaml
{
    "use_case": "layout",
    "textregions": {
        "paragraph": 1,
        "drop-capital": 1,
        "header": 2,
        "heading": 2,
        "marginalia": 3
    },
    "imageregion": 4,
    "separatorregion": 5,
    "graphicregions": {
        "rest_as_decoration": 6
    },
    "artificial_class_on_boundary": ["paragraph", "header", "heading", "marginalia"],
    "artificial_class_label": 7
}
```

This implies that the artificial class label, denoted by 7, will be present on PNG files and will only be added to the
elements labeled as "paragraph," "header," "heading," and "marginalia."

For "textline", "word", and "glyph", the artificial class on the boundaries will be activated only if the
"artificial_class_label" key is specified in the config file. Its value should be set as 2 since these elements
represent binary cases. For example, if the background and textline are denoted as 0 and 1 respectively, then the
artificial class should be assigned the value 2. The example JSON config file should look like this for "textline" use
case:

```yaml
{
    "use_case": "textline",
    "artificial_class_label": 2
}
```

If the coordinates of "PrintSpace" or "Border" are present in the page XML ground truth files, and the user wishes to
crop only the print space area, this can be achieved by activating the "-ps" argument. However, it should be noted that
in this scenario, since cropping will be applied to the label files, the directory of the original images must be
provided to ensure that they are cropped in sync with the labels. This ensures that the correct images and labels
required for training are obtained. The command should resemble the following:

```sh
eynollah-training generate-gt pagexml2label \
  -dx "dir of GT xml files" \
  -do "dir where output label png files will be written" \
  -cfg "custom config json file" \
  -to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels" \
  -ps \
  -di "dir where the org images are located" \
  -doi "dir where the cropped output images will be written"
```

## Train a model

### classification

For the classification use case, we haven't provided a ground truth generator, as it's unnecessary. For classification,
all we require is a training directory with subdirectories, each containing images of its respective classes. We need
separate directories for training and evaluation, and the class names (subdirectories) must be consistent across both
directories. Additionally, the class names should be specified in the config JSON file, as shown in the following
example. If, for instance, we aim to classify "apple" and "orange," with a total of 2 classes, the
"classification_classes_name" key in the config file should appear as follows:

```yaml
{
    "backbone_type" : "nontransformer",
    "task": "classification",
    "n_classes" : 2,
    "n_epochs" : 10,
    "input_height" : 448,
    "input_width" : 448,
    "weight_decay" : 1e-6,
    "n_batch" : 4,
    "learning_rate": 1e-4,
    "f1_threshold_classification": 0.8,
    "pretraining" : true,
    "classification_classes_name" : {"0":"apple",  "1":"orange"},
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

The "dir_train" should be like this:

```
.
└── train             # train directory
   ├── apple          # directory of images for apple class
   └── orange         # directory of images for orange class
```

And the "dir_eval" the same structure as train directory:

```
.
└── eval              # evaluation directory
   ├── apple          # directory of images for apple class
   └── orange         # directory of images for orange class

```

The classification model can be trained using the following command line:

```sh
eynollah-training train with config_classification.json
```

As evident in the example JSON file above, for classification, we utilize a "f1_threshold_classification" parameter.
This parameter is employed to gather all models with an evaluation f1 score surpassing this threshold. Subsequently,
an ensemble of these model weights is executed, and a model is saved in the output directory as "model_ens_avg".
Additionally, the weight of the best model based on the evaluation f1 score is saved as "model_best".

### reading order
An example config json file for machine based reading order should be like this:

```yaml
{
    "backbone_type" : "nontransformer",
    "task": "reading_order",
    "n_classes" : 1,
    "n_epochs" : 5,
    "input_height" : 672,
    "input_width" : 448,
    "weight_decay" : 1e-6,
    "n_batch" : 4,
    "learning_rate": 1e-4,
    "pretraining" : true,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

The "dir_train" should be like this:

```
.
└── train             # train directory
   ├── images          # directory of images
   └── labels         # directory of labels
```

And the "dir_eval" the same structure as train directory:

```
.
└── eval             # evaluation directory
   ├── images          # directory of images
   └── labels         # directory of labels
```

The classification model can be trained like the classification case command line.

### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement

#### Parameter configuration for segmentation or enhancement usecases

The following parameter configuration can be applied to all segmentation use cases and enhancements. The augmentation,
its sub-parameters, and continued training are defined only for segmentation use cases and enhancements, not for
classification and machine-based reading order, as you can see in their example config files.

* `backbone_type`: For segmentation tasks (such as text line, binarization, and layout detection) and enhancement, we
  offer two backbone options: a "nontransformer" and a "transformer" backbone. For the "transformer" backbone, we first
  apply a CNN followed by a transformer. In contrast, the "nontransformer" backbone utilizes only a CNN ResNet-50.
* `task`: The task parameter can have values such as "segmentation", "enhancement", "classification", and "reading_order".
* `patches`: If you want to break input images into smaller patches (input size of the model) you need to set this
* parameter to `true`. In the case that the model should see the image once, like page extraction, patches should be
  set to ``false``.
* `n_batch`: Number of batches at each iteration.
* `n_classes`: Number of classes. In the case of binary classification this should be 2. In the case of reading_order it
  should set to 1. And for the case of layout detection just the unique number of classes should be given.
* `n_epochs`: Number of epochs.
* `input_height`: This indicates the height of model's input.
* `input_width`: This indicates the width of model's input.
* `weight_decay`: Weight decay of l2 regularization of model layers.
* `pretraining`: Set to `true` to load pretrained weights of ResNet50 encoder. The downloaded weights should be saved
  in a folder named "pretrained_model" in the same directory of "train.py" script.
* `augmentation`: If you want to apply any kind of augmentation this parameter should first set to `true`.
* `flip_aug`: If `true`, different types of filp will be applied on image. Type of flips is given with "flip_index" parameter.
* `blur_aug`: If `true`, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" parameter.
* `scaling`: If `true`, scaling will be applied on image. Scale of scaling is given with "scales" parameter.
* `degrading`: If `true`, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" parameter.
* `brightening`: If `true`, brightening will be applied to the image. The amount of brightening is defined with "brightness" parameter.
* `rotation_not_90`: If `true`, rotation (not 90 degree) will be applied on image. Rotation angles are given with "thetha" parameter.
* `rotation`: If `true`, 90 degree rotation will be applied on image.
* `binarization`: If `true`,Otsu thresholding will be applied to augment the input data with binarized images.
* `scaling_bluring`: If `true`, combination of scaling and blurring will be applied on image.
* `scaling_binarization`: If `true`, combination of scaling and binarization will be applied on image.
* `scaling_flip`: If `true`, combination of scaling and flip will be applied on image.
* `flip_index`: Type of flips.
* `blur_k`: Type of blurrings.
* `scales`: Scales of scaling.
* `brightness`: The amount of brightenings.
* `thetha`: Rotation angles.
* `degrade_scales`: The amount of degradings.
* `continue_training`: If `true`, it means that you have already trained a  model and you would like to continue the
  training. So it is needed to providethe dir of trained model with "dir_of_start_model" and index for naming
  themodels. For example if you have already trained for 3 epochs then your lastindex is 2 and if you want to continue
  from model_1.h5, you can set `index_start` to 3 to start naming model with index 3.
* `weighted_loss`: If `true`, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to `true`the parameter "is_loss_soft_dice" should be ``false``
* `data_is_provided`: If you have already provided the input data you can set  this to `true`. Be sure that the train
  and eval data are in"dir_output".Since when once we provide training data we resize and augmentthem and then wewrite
  them in sub-directories train and eval in "dir_output".
* `dir_train`: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories.
* `index_start`: Starting index for saved models in the case that "continue_training"  is `true`.
* `dir_of_start_model`: Directory containing pretrained model to continue training the model in the case that "continue_training"  is `true`.
* `transformer_num_patches_xy`: Number of patches for vision transformer in x and y direction respectively.
* `transformer_patchsize_x`: Patch size of vision transformer patches in x direction.
* `transformer_patchsize_y`: Patch size of vision transformer patches in y direction.
* `transformer_projection_dim`: Transformer projection dimension. Default value is 64.
* `transformer_mlp_head_units`: Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64].
* `transformer_layers`: transformer layers. Default value is 8.
* `transformer_num_heads`: Transformer number of heads. Default value is 4.
* `transformer_cnn_first`: We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true.

In the case of segmentation and enhancement the train and evaluation directory should be as following.

The "dir_train" should be like this:

```
.
└── train             # train directory
   ├── images          # directory of images
   └── labels         # directory of labels
```

And the "dir_eval" the same structure as train directory:

```
.
└── eval             # evaluation directory
   ├── images          # directory of images
   └── labels         # directory of labels
```

After configuring the JSON file for segmentation or enhancement, training can be initiated by running the following
command, similar to the process for classification and reading order:

```
eynollah-training train with config_classification.json`
```

#### Binarization

### Ground truth format

Lables for each pixel are identified by a number. So if you have a 
binary case, ``n_classes`` should be set to ``2`` and labels should 
be ``0`` and ``1`` for each class and pixel.

In the case of multiclass, just set ``n_classes`` to the number of classes 
you have and the try to produce the labels by pixels set from ``0 , 1 ,2 .., n_classes-1``.
The labels format should be png. 
Our lables are 3 channel png images but only information of first channel is used. 
If you have an image label with height and width of 10, for a binary case the first channel should look like this:
    
    Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             ...,
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ] 
    
 This means that you have an image by `10*10*3` and `pixel[0,0]` belongs
 to class `1` and `pixel[0,1]` belongs to class `0`.
 
 A small sample of training data for binarization experiment can be found here, [Training data sample](https://qurator-data.de/~vahid.rezanezhad/binarization_training_data_sample/), which contains images and lables folders.
    

An example config json file for binarization can be like this:

```yaml
{
    "backbone_type" : "transformer",
    "task": "binarization",
    "n_classes" : 2,
    "n_epochs" : 4,
    "input_height" : 224,
    "input_width" : 672,
    "weight_decay" : 1e-6,
    "n_batch" : 1,
    "learning_rate": 1e-4,
    "patches" : true,
    "pretraining" : true,
    "augmentation" : true,
    "flip_aug" : false,
    "blur_aug" : false,
    "scaling" : true,
    "degrading": false,
    "brightening": false,
    "binarization" : false,
    "scaling_bluring" : false,
    "scaling_binarization" : false,
    "scaling_flip" : false,
    "rotation": false,
    "rotation_not_90": false,
    "transformer_num_patches_xy": [7, 7],
    "transformer_patchsize_x": 3,
    "transformer_patchsize_y": 1,
    "transformer_projection_dim": 192,
    "transformer_mlp_head_units": [128, 64],
    "transformer_layers": 8,
    "transformer_num_heads": 4,
    "transformer_cnn_first": true,
    "blur_k" : ["blur","guass","median"],
    "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
    "brightness" : [1.3, 1.5, 1.7, 2],
    "degrade_scales" : [0.2, 0.4],
    "flip_index" : [0, 1, -1],
    "thetha" : [10, -10],
    "continue_training": false,
    "index_start" : 0,
    "dir_of_start_model" : " ",
    "weighted_loss": false,
    "is_loss_soft_dice": false,
    "data_is_provided": false,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

#### Textline

```yaml
{
    "backbone_type" : "nontransformer",
    "task": "segmentation",
    "n_classes" : 2,
    "n_epochs" : 4,
    "input_height" : 448,
    "input_width" : 224,
    "weight_decay" : 1e-6,
    "n_batch" : 1,
    "learning_rate": 1e-4,
    "patches" : true,
    "pretraining" : true,
    "augmentation" : true,
    "flip_aug" : false,
    "blur_aug" : false,
    "scaling" : true,
    "degrading": false,
    "brightening": false,
    "binarization" : false,
    "scaling_bluring" : false,
    "scaling_binarization" : false,
    "scaling_flip" : false,
    "rotation": false,
    "rotation_not_90": false,
    "blur_k" : ["blur","guass","median"],
    "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
    "brightness" : [1.3, 1.5, 1.7, 2],
    "degrade_scales" : [0.2, 0.4],
    "flip_index" : [0, 1, -1],
    "thetha" : [10, -10],
    "continue_training": false,
    "index_start" : 0,
    "dir_of_start_model" : " ",
    "weighted_loss": false,
    "is_loss_soft_dice": false,
    "data_is_provided": false,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

#### Enhancement

```yaml
{
    "backbone_type" : "nontransformer",
    "task": "enhancement",
    "n_classes" : 3,
    "n_epochs" : 4,
    "input_height" : 448,
    "input_width" : 224,
    "weight_decay" : 1e-6,
    "n_batch" : 4,
    "learning_rate": 1e-4,
    "patches" : true,
    "pretraining" : true,
    "augmentation" : true,
    "flip_aug" : false,
    "blur_aug" : false,
    "scaling" : true,
    "degrading": false,
    "brightening": false,
    "binarization" : false,
    "scaling_bluring" : false,
    "scaling_binarization" : false,
    "scaling_flip" : false,
    "rotation": false,
    "rotation_not_90": false,
    "blur_k" : ["blur","guass","median"],
    "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
    "brightness" : [1.3, 1.5, 1.7, 2],
    "degrade_scales" : [0.2, 0.4],
    "flip_index" : [0, 1, -1],
    "thetha" : [10, -10],
    "continue_training": false,
    "index_start" : 0,
    "dir_of_start_model" : " ",
    "weighted_loss": false,
    "is_loss_soft_dice": false,
    "data_is_provided": false,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

It's important to mention that the value of n_classes for enhancement should be 3, as the model's output is a 3-channel
image.

#### Page extraction

```yaml
{
    "backbone_type" : "nontransformer",
    "task": "segmentation",
    "n_classes" : 2,
    "n_epochs" : 4,
    "input_height" : 448,
    "input_width" : 224,
    "weight_decay" : 1e-6,
    "n_batch" : 1,
    "learning_rate": 1e-4,
    "patches" : false,
    "pretraining" : true,
    "augmentation" : false,
    "flip_aug" : false,
    "blur_aug" : false,
    "scaling" : true,
    "degrading": false,
    "brightening": false,
    "binarization" : false,
    "scaling_bluring" : false,
    "scaling_binarization" : false,
    "scaling_flip" : false,
    "rotation": false,
    "rotation_not_90": false,
    "blur_k" : ["blur","guass","median"],
    "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
    "brightness" : [1.3, 1.5, 1.7, 2],
    "degrade_scales" : [0.2, 0.4],
    "flip_index" : [0, 1, -1],
    "thetha" : [10, -10],
    "continue_training": false,
    "index_start" : 0,
    "dir_of_start_model" : " ",
    "weighted_loss": false,
    "is_loss_soft_dice": false,
    "data_is_provided": false,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```

For page segmentation (or print space or border segmentation), the model needs to view the input image in its
entirety,hence the patches parameter should be set to false.

#### layout segmentation

An example config json file for layout segmentation with 5 classes (including background) can be like this:

```yaml
{
    "backbone_type" : "transformer",
    "task": "segmentation",
    "n_classes" : 5,
    "n_epochs" : 4,
    "input_height" : 448,
    "input_width" : 224,
    "weight_decay" : 1e-6,
    "n_batch" : 1,
    "learning_rate": 1e-4,
    "patches" : true,
    "pretraining" : true,
    "augmentation" : true,
    "flip_aug" : false,
    "blur_aug" : false,
    "scaling" : true,
    "degrading": false,
    "brightening": false,
    "binarization" : false,
    "scaling_bluring" : false,
    "scaling_binarization" : false,
    "scaling_flip" : false,
    "rotation": false,
    "rotation_not_90": false,
    "transformer_num_patches_xy": [7, 14],
    "transformer_patchsize_x": 1,
    "transformer_patchsize_y": 1,
    "transformer_projection_dim": 64,
    "transformer_mlp_head_units": [128, 64],
    "transformer_layers": 8,
    "transformer_num_heads": 4,
    "transformer_cnn_first": true,
    "blur_k" : ["blur","guass","median"],
    "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
    "brightness" : [1.3, 1.5, 1.7, 2],
    "degrade_scales" : [0.2, 0.4],
    "flip_index" : [0, 1, -1],
    "thetha" : [10, -10],
    "continue_training": false,
    "index_start" : 0,
    "dir_of_start_model" : " ",
    "weighted_loss": false,
    "is_loss_soft_dice": false,
    "data_is_provided": false,
    "dir_train": "./train",
    "dir_eval": "./eval",
    "dir_output": "./output"
}
```
## Inference with the trained model

### classification

For conducting inference with a trained model, you simply need to execute the following command line, specifying the
directory of the model and the image on which to perform inference:

```sh
eynollah-training inference -m "model dir" -i "image"
```

This will straightforwardly return the class of the image.

### machine based reading order

To infer the reading order using a reading order model, we need a page XML file containing layout information but
without the reading order. We simply need to provide the model directory, the XML file, and the output directory. The
new XML file with the added reading order will be written to the output directory with the same name. We need to run:

```sh
eynollah-training inference \
  -m "model dir" \
  -xml "page xml file" \
  -o "output dir to write new xml with reading order"
```

### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement

For conducting inference with a trained model for segmentation and enhancement you need to run the following command line:

```sh
eynollah-training inference \
  -m "model dir" \
  -i "image" \
  -p \
  -s "output image"
```

Note that in the case of page extraction the -p flag is not needed.

For segmentation or binarization tasks, if a ground truth (GT) label is available, the IoU evaluation metric can be
calculated for the output. To do this, you need to provide the GT label using the argument -gt.
