qurator-spk/eynollah
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* background, page border, text region, text line, header, image, separator, marginalia, initial (drop capital), table * background, page border, text region, text line, header, image, separator, marginalia, initial (drop capital), table
* Support for various image optimization operations: * Support for various image optimization operations:
* cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
* Text line segmentation to bounding boxes or polygons (contours) including curved lines and vertical text * Text line segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
* Detection of reading order * Detection of reading order
* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) format * Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
## Installation ## Installation
Python versions `3.7-3.10` with Tensorflow `>=2.4` are currently supported. Python versions `3.7-3.10` with Tensorflow `>=2.4` are currently supported.
For (minimal) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed. For (limited) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed.
You can either install via You can either install via
@ -43,8 +43,6 @@ Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data
Alternatively, running `make models` will download and extract models to `$(PWD)/models_eynollah`. Alternatively, running `make models` will download and extract models to `$(PWD)/models_eynollah`.
### Training
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). 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 ## Usage
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The following options can be used to further configure the processing: The following options can be used to further configure the processing:
``` | option | description |
-fl perform full layout analysis including detection of headers and drop capitals |----------|:-------------|
-tab try to detect tables | `-fl` | apply full layout analysis including all steps and segmentation classes |
-light apply a faster but simpler method for main region detection and deskewing | `-light` | apply a lighter and faster but simpler method for main region detection and deskewing |
-ae allow resizing and enhancing the input image, the enhanced image is saved to the output directory | `-tab` | apply table detection |
-as allow scaling - automatically check whether the input image needs scaling or not | `-ae` | apply enhancement (the resulting image is saved to the output directory) |
-ib allow binarization of the input image | `-as` | apply scaling |
-ho ignore headers for reading order prediction | `-ib` | apply binarization (the resulting image is saved to the output directory) |
-cl extract contours of curved textlines instead of rectangle bounding boxes | `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
-ep enables plotting. This MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae` options | `-ho` | ignore headers for reading order dectection |
-di <directory> process all images in a directory in batch mode | `-di <directory>` | process all images in a directory in batch mode |
-si <directory> save image regions detected in documents to this directory | `-si <directory>` | save image regions detected in documents to this directory |
-sd <directory> save deskewed image to this directory | `-sd <directory>` | save deskewed image to this directory |
-sl <directory> save layout prediction as plot to this directory | `-sl <directory>` | save layout prediction as plot to this directory |
-sa <directory> save all outputs (plot, enhanced or binary image and layout prediction) to this directory | `-sa <directory>` | save all (plot, enhanced, binary image and layout prediction) to this directory |
```
The tool performs better with RGB images as input than with greyscale or binarized images. The tool performs better with RGB images as input than with greyscale or binarized images.