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# Eynollah
> Perform document layout analysis (segmentation) from image data and return the results as [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
> Document Layout Analysis (segmentation) using pre-trained models and heuristics
[![PyPI Version](https://img.shields.io/pypi/v/eynollah)](https://pypi.org/project/eynollah/)
[![CircleCI Build Status](https://circleci.com/gh/qurator-spk/eynollah.svg?style=shield)](https://circleci.com/gh/qurator-spk/eynollah)
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![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg)
## Features
* Support for up to 10 segmentation classes:
* background, page border, text region, text line, header, image, separator, marginalia, initial (drop capital), table
* Support for various image optimization operations:
* cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
* Text line segmentation to bounding boxes or polygons (contours) including curved lines and vertical text
* Detection of reading order
* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) format
## Installation
`pip install .` or
Python versions `3.7-3.10` with Tensorflow `>=2.4` are currently supported.
`pip install -e .` for editable installation
For (minimal) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed.
Alternatively, you can also use `make` with these targets:
You can either install via
`make install` or
```
pip install eynollah
```
`make install-dev` for editable installation
or clone the repository, enter it and install (editable) with
The current version of Eynollah runs on Python `>=3.7` with Tensorflow `>=2.4`.
```
git clone git@github.com:qurator-spk/eynollah.git
cd eynollah; pip install -e .
```
In order to use a GPU for inference, the CUDA toolkit version 10.x needs to be installed.
Alternatively, you can run `make install` or `make install-dev` for editable installation.
### Models
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/).
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/).
Alternatively, running `make models` will download and extract models to `$(PWD)/models_eynollah`.
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The command-line interface can be called like this:
```sh
eynollah -i <image file name> -o <directory to write output> -m <directory of models> [OPTIONS]
eynollah \
-i <image file> \
-o <output directory> \
-m <path to directory containing model files> \
[OPTIONS]
```
The following options can be used to further configure the processing:
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</details>
</details>