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updating eynollah README, how to use it for use cases
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README.md
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README.md
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@ -50,10 +50,16 @@ For documentation on methods and models, have a look at [`models.md`](https://gi
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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).
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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).
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## Usage
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## Usage
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The command-line interface can be called like this:
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Eynollah has four key use cases: layout analysis, binarization, OCR, and machine-based reading order.
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### Layout
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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.
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The command-line interface for layout can be called like this:
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```sh
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```sh
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eynollah \
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eynollah layout \
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-i <single image file> | -di <directory containing image files> \
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-i <single image file> | -di <directory containing image files> \
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-o <output directory> \
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-o <output directory> \
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-m <directory containing model files> \
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-m <directory containing model files> \
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@ -66,6 +72,7 @@ The following options can be used to further configure the processing:
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|-------------------|:-------------------------------------------------------------------------------|
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|-------------------|:-------------------------------------------------------------------------------|
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| `-fl` | full layout analysis including all steps and segmentation classes |
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| `-fl` | full layout analysis including all steps and segmentation classes |
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| `-light` | lighter and faster but simpler method for main region detection and deskewing |
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| `-light` | lighter and faster but simpler method for main region detection and deskewing |
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| `-tll` | this indicates the light textline and should be passed with light version |
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| `-tab` | apply table detection |
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| `-tab` | apply table detection |
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| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
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| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
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| `-as` | apply scaling |
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| `-as` | apply scaling |
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@ -83,6 +90,34 @@ The following options can be used to further configure the processing:
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If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
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If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
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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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The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
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### Binarization
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Document Image Binarization
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The command-line interface for binarization of single image can be called like this:
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```sh
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eynollah binarization \
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-m <path to directory containing model files> \
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<input image> \
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<output image>
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```
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and for flowing from a directory like this:
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```sh
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eynollah binarization \
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-m <path to directory containing model files> \
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-di <directory containing image files> \
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-do <output directory>
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```
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### OCR
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Under development
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### Machine-based-reading-order
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Under development
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#### Use as OCR-D processor
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#### Use as OCR-D 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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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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