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# Eynollah
> Document Layout Analysis (segmentation) using pre-trained models and heuristics
> Document Layout Analysis with Deep Learning 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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* [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface
## Installation
Python versions `3.8-3.11` with Tensorflow versions >=`2.12` on Linux are currently supported. Unfortunately we can not currently support Windows or MacOS.
Windows users may be able to successfully run the tool through [WSL](https://learn.microsoft.com/en-us/windows/wsl/).
Python versions `3.8-3.11` with Tensorflow versions >=`2.12` on Linux are currently supported. While we can not provide support for Windows or MacOS, Windows users may be able to install and run the tool through Linux in [WSL](https://learn.microsoft.com/en-us/windows/wsl/).
For (limited) GPU support the CUDA toolkit needs to be installed.
You can either install via
You can either install from PyPI via
```
pip install eynollah
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cd eynollah; pip install -e .
```
Alternatively, you can run `make install` or `make install-dev` for editable installation.
Alternatively, run `make install` or `make install-dev` for editable installation.
## Models
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/).
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).
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/). In case you want to train your own model with Eynollah, have a look at [`train`](https://github.com/qurator-spk/eynollah/tree/main/eynollah/eynollah/train).
## Usage
The command-line interface can be called like this: