small fixes to main readme

This commit is contained in:
cneud 2025-10-20 23:19:10 +02:00
parent 230e7cc705
commit 7d70835d22
2 changed files with 15 additions and 11 deletions

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@ -52,24 +52,25 @@ pip install "eynollah[OCR]"
make install EXTRAS=OCR make install EXTRAS=OCR
``` ```
With Docker, use ### Docker
Use
``` ```
docker pull ghcr.io/qurator-spk/eynollah:latest docker pull ghcr.io/qurator-spk/eynollah:latest
``` ```
For additional documentation on using Eynollah and Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md). When using Eynollah with Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md).
## Models ## Models
Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah). Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).
For documentation on models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md). For model documentation and model cards, see [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
## Training ## Training
To train your own model with Eynollah, see the documentation in [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md) and use the To train your own model with Eynollah, see [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md) and use the tools in the [`train`](https://github.com/qurator-spk/eynollah/tree/main/train) folder.
tools in the [`train`](https://github.com/qurator-spk/eynollah/tree/main/train) folder.
## Usage ## Usage
@ -83,10 +84,7 @@ Eynollah supports five use cases:
### Layout Analysis ### Layout Analysis
The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
order using either heuristic methods or a [pretrained reading order detection model](https://github.com/qurator-spk/eynollah#machine-based-reading-order). order using heuristic methods or a [pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
development, based on pre-existing layout analysis results in PAGE-XML format as input.
The command-line interface for layout analysis can be called like this: The command-line interface for layout analysis can be called like this:
@ -156,6 +154,8 @@ eynollah ocr \
``` ```
### Reading Order Detection ### Reading Order Detection
Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
development, based on pre-existing layout analysis data in PAGE-XML format as input.
The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files. The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
@ -169,6 +169,10 @@ eynollah machine-based-reading-order \
-o <output directory> -o <output directory>
``` ```
## Use as OCR-D processor
See [`ocrd.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
## How to cite ## How to cite
```bibtex ```bibtex

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@ -32,12 +32,12 @@ Alternatively, just "log in" to the container once and use the commands there:
## Training with Docker ## Training with Docker
Build the Docker image Build the Docker training image
cd train cd train
docker build -t model-training . docker build -t model-training .
Run the Docker image Run the Docker training image
cd train cd train
docker run --gpus all -v $PWD:/entry_point_dir model-training docker run --gpus all -v $PWD:/entry_point_dir model-training