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resolve some comments from review

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README.md
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@ -19,7 +19,8 @@
* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
* [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface
:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of
historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
## Installation
Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported.
@ -42,7 +43,7 @@ cd eynollah; pip install -e .
Alternatively, you can run `make install` or `make install-dev` for editable installation.
## Models
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
Pretrained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
For documentation on methods and models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
@ -50,13 +51,17 @@ For documentation on methods and models, have a look at [`models.md`](https://gi
In case you want to train your own model with Eynollah, have a look at [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md).
## Usage
Eynollah supports four use cases: layout analysis (segmentation), binarization, text recognition (OCR),
and (trainable) reading order detection.
Eynollah has four key use cases: layout analysis, binarization, OCR, and machine-based reading order.
### Layout Analysis
The layout analysis module is responsible for detecting layouts, identifying text lines, and determining reading order
using both heuristic methods or a machine-based reading order detection model.
### Layout
The layout module is responsible for detecting layouts, identifying text lines, and determining reading order using both heuristic methods or a machine-based reading order detection model. It's important to note that this functionality should not be confused with the machine-based-reading-order use case. The latter, still under development, focuses specifically on determining the reading order for a given layout in an XML file. In contrast, layout detection takes an image as input, and after detecting the layout, it can also determine the reading order using a machine-based model.
Note that there are currently two supported ways for reading order detection: either as part of layout analysis based
on image input, or, currently under development, for given layout analysis results based on PAGE-XML data as input.
The command-line interface for layout can be called like this:
The command-line interface for layout analysis can be called like this:
```sh
eynollah layout \
@ -87,18 +92,19 @@ The following options can be used to further configure the processing:
| `-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).
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.
### Binarization
Document Image Binarization
The binarization module performs document image binarization using pretrained pixelwise segmentation models.
The command-line interface for binarization of single image can be called like this:
```sh
eynollah binarization \
-m <path to directory containing model files> \
<input image> \
-m <directory containing model files> \
<single image file> \
<output image>
```
@ -117,9 +123,7 @@ Under development
### Machine-based-reading-order
Under development
#### Use as OCR-D processor
Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli),
formally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json).
@ -127,7 +131,6 @@ In this case, the source image file group with (preferably) RGB images should be
ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models 2022-04-05
If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows:
- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results)
- existing annotation (and respective `AlternativeImage`s) are partially _ignored_:
@ -138,7 +141,6 @@ If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynol
(because some other preprocessing step was in effect like `denoised`), then
the output PAGE-XML will be based on that as new top-level (`@imageFilename`)
ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models 2022-04-05
Still, in general, it makes more sense to add other workflow steps **after** Eynollah.