:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
## 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 `3.8-3.11` with Tensorflow `2.12-2.15` on Linux are currently supported.
For (limited) GPU support the CUDA toolkit needs to be installed.
You can either install via
You can either install from PyPI
```
pip install eynollah
@ -40,25 +41,28 @@ cd eynollah; pip install -e .
Alternatively, you can 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/).
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
## Train
🚧 **Work in progress**
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, have a look at [`sbb_pixelwise_segmentation`](https://github.com/qurator-spk/sbb_pixelwise_segmentation).
## Usage
The command-line interface can be called like this: