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update readme (OCR-D section)
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README.md
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README.md
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@ -83,23 +83,27 @@ If no option is set, the tool performs layout detection of main regions (backgro
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The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
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#### Use as OCR-D processor
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🚧 **Work in progress**
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Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) processor.
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Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli).
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In this case, the source image file group with (preferably) RGB images should be used as input like this:
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```
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ocrd-eynollah-segment -I OCR-D-IMG -O SEG-LINE -P models
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```
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Any image referenced by `@imageFilename` in PAGE-XML is passed on directly to Eynollah as a processor, so that e.g.
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ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models 2022-04-05
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```
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ocrd-eynollah-segment -I OCR-D-IMG-BIN -O SEG-LINE -P models
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```
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uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps
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If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows:
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- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results)
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- existing annotation (and respective `AlternativeImage`s) are partially _ignored_:
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- previous page frame detection (`cropped` images)
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- previous derotation (`deskewed` images)
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- previous thresholding (`binarized` images)
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- if the page-level image nevertheless deviates from the original (`@imageFilename`)
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(because some other preprocessing step was in effect like `denoised`), then
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the output PAGE-XML will be based on that as new top-level (`@imageFilename`)
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ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models 2022-04-05
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Still, in general, it makes more sense to add other workflow steps **after** Eynollah.
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#### Additional documentation
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Please check the [wiki](https://github.com/qurator-spk/eynollah/wiki).
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