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@ -38,6 +38,11 @@ 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/).
In case you want to train your own model to use with Eynollah, have a look at [sbb_pixelwise_segmentation](https://github.com/qurator-spk/sbb_pixelwise_segmentation).
## Usage
The command-line interface can be called like this:
@ -70,13 +75,7 @@ The following options can be used to further configure the processing:
| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
If no option is set, the tool will perform layout detection of main regions (background, text, images, separators and marginals).
The tool produces better output from RGB images as input than greyscale or binarized images.
## Models
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/).
In case you want to train your own model to use with Eynollah, have a look at [sbb_pixelwise_segmentation](https://github.com/qurator-spk/sbb_pixelwise_segmentation).
The tool produces better quality output when RGB images are used as input than greyscale or binarized images.
#### Use as OCR-D processor
@ -88,10 +87,10 @@ In this case, the source image file group with (preferably) RGB images should be
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. calling
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
```
still uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps
uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps