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# Eynollah
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> Perform document layout analysis (segmentation) from image data and return the results as [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
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> Document Layout Analysis (segmentation) using pre-trained models and heuristics
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[](https://pypi.org/project/eynollah/)
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[](https://circleci.com/gh/qurator-spk/eynollah)
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@ -8,24 +8,38 @@
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## Features
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* Support for up to 10 segmentation classes:
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* background, page border, text region, text line, header, image, separator, marginalia, initial (drop capital), table
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* Support for various image optimization operations:
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* cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
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* Text line segmentation to bounding boxes or polygons (contours) including curved lines and vertical text
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* Detection of reading order
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* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) format
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## Installation
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`pip install .` or
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Python versions `3.7-3.10` with Tensorflow `>=2.4` are currently supported.
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`pip install -e .` for editable installation
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For (minimal) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed.
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Alternatively, you can also use `make` with these targets:
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You can either install via
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`make install` or
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```
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pip install eynollah
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```
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`make install-dev` for editable installation
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or clone the repository, enter it and install (editable) with
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The current version of Eynollah runs on Python `>=3.7` with Tensorflow `>=2.4`.
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```
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git clone git@github.com:qurator-spk/eynollah.git
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cd eynollah; pip install -e .
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```
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In order to use a GPU for inference, the CUDA toolkit version 10.x needs to be installed.
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Alternatively, you can run `make install` or `make install-dev` for editable installation.
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### Models
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In order to run this tool you need trained models. You can download our pretrained models from [qurator-data.de](https://qurator-data.de/eynollah/).
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Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/).
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Alternatively, running `make models` will download and extract models to `$(PWD)/models_eynollah`.
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@ -38,7 +52,11 @@ In case you want to train your own model to use with Eynollah, have a look at [s
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The command-line interface can be called like this:
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```sh
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eynollah -i <image file name> -o <directory to write output> -m <directory of models> [OPTIONS]
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eynollah \
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-i <image file> \
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-o <output directory> \
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-m <path to directory containing model files> \
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[OPTIONS]
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```
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The following options can be used to further configure the processing:
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@ -182,5 +200,4 @@ would still use the original (RGB) image despite any binarization that may have
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</details>
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</details>
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</details>
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cneud
1 year ago