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122 lines
6.5 KiB
Markdown
122 lines
6.5 KiB
Markdown
# Eynollah
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> Document Layout Analysis with Deep Learning and Heuristics
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[](https://pypi.org/project/eynollah/)
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[](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml)
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[](https://opensource.org/license/apache-2-0/)
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[](https://doi.org/10.1145/3604951.3605513)
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## Features
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* Support for up to 10 segmentation classes:
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* background, [page border](https://ocr-d.de/en/gt-guidelines/trans/lyRand.html), [text region](https://ocr-d.de/en/gt-guidelines/trans/lytextregion.html#textregionen__textregion_), [text line](https://ocr-d.de/en/gt-guidelines/pagexml/pagecontent_xsd_Complex_Type_pc_TextLineType.html), [header](https://ocr-d.de/en/gt-guidelines/trans/lyUeberschrift.html), [image](https://ocr-d.de/en/gt-guidelines/trans/lyBildbereiche.html), [separator](https://ocr-d.de/en/gt-guidelines/trans/lySeparatoren.html), [marginalia](https://ocr-d.de/en/gt-guidelines/trans/lyMarginalie.html), [initial](https://ocr-d.de/en/gt-guidelines/trans/lyInitiale.html), [table](https://ocr-d.de/en/gt-guidelines/trans/lyTabellen.html)
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* Support for various image optimization operations:
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* cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
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* Text line segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
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* Detection of reading order (left-to-right or right-to-left)
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* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
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* [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface
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: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.
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## Installation
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Python `3.8-3.11` with Tensorflow `2.12-2.15` on Linux are currently supported.
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For (limited) GPU support the CUDA toolkit needs to be installed.
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You can either install from PyPI
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```
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pip install eynollah
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```
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or clone the repository, enter it and install (editable) with
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```
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git clone git@github.com:qurator-spk/eynollah.git
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cd eynollah; pip install -e .
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```
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Alternatively, you can run `make install` or `make install-dev` for editable installation.
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## Models
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Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
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## Train
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🚧 **Work in progress**
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In case you want to train your own model, have a look at [`sbb_pixelwise_segmentation`](https://github.com/qurator-spk/sbb_pixelwise_segmentation).
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## Usage
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The command-line interface can be called like this:
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```sh
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eynollah \
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-i <single image file> | -di <directory containing image files> \
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-o <output directory> \
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-m <directory containing model files> \
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[OPTIONS]
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```
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The following options can be used to further configure the processing:
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| option | description |
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|-------------------|:-------------------------------------------------------------------------------|
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| `-fl` | full layout analysis including all steps and segmentation classes |
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| `-light` | lighter and faster but simpler method for main region detection and deskewing |
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| `-tab` | apply table detection |
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| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
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| `-as` | apply scaling |
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| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
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| `-ib` | apply binarization (the resulting image is saved to the output directory) |
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| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
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| `-ho` | ignore headers for reading order dectection |
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| `-si <directory>` | save image regions detected to this directory |
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| `-sd <directory>` | save deskewed image to this directory |
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| `-sl <directory>` | save layout prediction as plot to this directory |
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| `-sp <directory>` | save cropped page image to this directory |
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| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
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If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
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The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
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#### Use as OCR-D processor
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🚧 **Work in progress**
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Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) processor.
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In this case, the source image file group with (preferably) RGB images should be used as input like this:
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```
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ocrd-eynollah-segment -I OCR-D-IMG -O SEG-LINE -P models
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```
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Any image referenced by `@imageFilename` in PAGE-XML is passed on directly to Eynollah as a processor, so that e.g.
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```
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ocrd-eynollah-segment -I OCR-D-IMG-BIN -O SEG-LINE -P models
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```
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uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps
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#### Additional documentation
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Please check the [wiki](https://github.com/qurator-spk/eynollah/wiki).
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## How to cite
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If you find this tool useful in your work, please consider citing our paper:
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```bibtex
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@inproceedings{hip23rezanezhad,
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title = {Document Layout Analysis with Deep Learning and Heuristics},
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author = {Rezanezhad, Vahid and Baierer, Konstantin and Gerber, Mike and Labusch, Kai and Neudecker, Clemens},
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booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,
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San José, CA, USA, August 25-26, 2023},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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year = {2023},
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pages = {73--78},
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url = {https://doi.org/10.1145/3604951.3605513}
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}
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```
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