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small fixes to main readme
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2 changed files with 15 additions and 11 deletions
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README.md
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README.md
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@ -52,24 +52,25 @@ pip install "eynollah[OCR]"
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make install EXTRAS=OCR
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make install EXTRAS=OCR
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```
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```
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With Docker, use
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### Docker
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Use
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```
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```
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docker pull ghcr.io/qurator-spk/eynollah:latest
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docker pull ghcr.io/qurator-spk/eynollah:latest
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```
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```
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For additional documentation on using Eynollah and Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md).
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When using Eynollah with Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md).
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## Models
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## Models
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Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).
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Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).
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For documentation on models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
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For model documentation and model cards, see [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
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## Training
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## Training
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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
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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.
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tools in the [`train`](https://github.com/qurator-spk/eynollah/tree/main/train) folder.
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## Usage
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## Usage
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@ -83,10 +84,7 @@ Eynollah supports five use cases:
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### Layout Analysis
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### Layout Analysis
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The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
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The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
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order using either heuristic methods or a [pretrained reading order detection model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
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order using heuristic methods or a [pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
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Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
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development, based on pre-existing layout analysis results in PAGE-XML format as input.
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The command-line interface for layout analysis can be called like this:
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The command-line interface for layout analysis can be called like this:
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@ -156,6 +154,8 @@ eynollah ocr \
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```
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```
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### Reading Order Detection
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### Reading Order Detection
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Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
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development, based on pre-existing layout analysis data in PAGE-XML format as input.
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The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
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The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
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@ -169,6 +169,10 @@ eynollah machine-based-reading-order \
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-o <output directory>
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-o <output directory>
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```
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```
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## Use as OCR-D processor
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See [`ocrd.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
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## How to cite
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## How to cite
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```bibtex
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```bibtex
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@ -32,12 +32,12 @@ Alternatively, just "log in" to the container once and use the commands there:
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## Training with Docker
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## Training with Docker
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Build the Docker image
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Build the Docker training image
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cd train
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cd train
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docker build -t model-training .
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docker build -t model-training .
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Run the Docker image
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Run the Docker training image
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cd train
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cd train
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docker run --gpus all -v $PWD:/entry_point_dir model-training
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docker run --gpus all -v $PWD:/entry_point_dir model-training
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