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consolidate usage documentation

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@ -16,8 +16,10 @@ Two Arabic/Persian terms form the name of the model suite: عين الله, whic
"eynollah"; it translates into English as "God's Eye" -- it sees (nearly) everything on the document image.
See the flowchart below for the different stages and how they interact:
![](https://user-images.githubusercontent.com/952378/100619946-1936f680-331e-11eb-9297-6e8b4cab3c16.png)
## Models
### Image enhancement

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# Usage documentation
The command-line interface can be called like this:
```sh
eynollah \
-i <single image file> | -di <directory containing image files> \
-o <output directory> \
-m <directory containing model files> \
[OPTIONS]
```
The following options can be used to further configure the processing:
| option | description |
|-------------------|:-------------------------------------------------------------------------------|
| `-fl` | full layout analysis including all steps and segmentation classes |
| `-light` | lighter and faster but simpler method for main region detection and deskewing |
| `-tab` | apply table detection |
| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
| `-as` | apply scaling |
| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
| `-ib` | apply binarization (the resulting image is saved to the output directory) |
| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
| `-eoi` | extract only images to output directory (other processing will not be done) |
| `-ho` | ignore headers for reading order dectection |
| `-si <directory>` | save image regions detected to this directory |
| `-sd <directory>` | save deskewed image to this directory |
| `-sl <directory>` | save layout prediction as plot to this directory |
| `-sp <directory>` | save cropped page image to this directory |
| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
### `--full-layout` vs `--no-full-layout`
Here are the difference in elements detected depending on the `--full-layout`/`--no-full-layout` command line flags:
| | `--full-layout` | `--no-full-layout` |
|--------------------------|-----------------|--------------------|
| reading order | x | x |
| header regions | x | - |
| text regions | x | x |
| text regions / text line | x | x |
| drop-capitals | x | - |
| marginals | x | x |
| marginals / text line | x | x |
| image region | x | x |
## Use as OCR-D processor
Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) processor that is described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json).
The source image file group with (preferably) RGB images should be used as input for Eynollah like this:
```
ocrd-eynollah-segment -I OCR-D-IMG -O SEG-LINE -P models
```
Any image referenced by `@imageFilename` in PAGE-XML is passed on directly to Eynollah as a processor, so that e.g.
```
ocrd-eynollah-segment -I OCR-D-IMG-BIN -O SEG-LINE -P models
```
uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps.
## Use with Docker
TODO
## Hints
* If none of the parameters is set to `true`, the tool will perform a layout detection of main regions (background,
text, images, separators and marginals). An advantage of this tool is that it tries to extract main text regions
separately as much as possible.
* If you set `-ae` (**a**llow image **e**nhancement) parameter to `true`, the tool will first check the ppi
(pixel-per-inch) of the image and when it is less than 300, the tool will resize it and only then image enhancement will
occur. Image enhancement can also take place without this option, but by setting this option to `true`, the layout xml
data (e.g. coordinates) will be based on the resized and enhanced image instead of the original image.
* For some documents, while the quality is good, their scale is very large, and the performance of tool decreases. In
such cases you can set `-as` (**a**llow **s**caling) to `true`. With this option enabled, the tool will try to rescale
the image and only then the layout detection process will begin.
* If you care about drop capitals (initials) and headings, you can set `-fl` (**f**ull **l**ayout) to `true`. With this
setting, the tool can currently distinguish 7 document layout classes/elements.
* In cases where the document includes curved headers or curved lines, rectangular bounding boxes for textlines will not
be a great option. In such cases it is strongly recommended setting the flag `-cl` (**c**urved **l**ines) to `true` to
find contours of curved lines instead of rectangular bounding boxes. Be advised that enabling this option increases the
processing time of the tool.
* To crop and save image regions inside the document, set the parameter `-si` (**s**ave **i**mages) to true and provide
a directory path to store the extracted images.
* To extract only images from a document, set the parameter `-eoi` (**e**xtract **o**nly **i**mages). Choosing this
option disables any other processing. To save the cropped images add `-ep` and `-si`.