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Version](https://img.shields.io/pypi/v/eynollah)](https://pypi.org/project/eynollah/)\n[![GH Actions Test](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml/badge.svg)](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml)\n[![GH Actions Deploy](https://github.com/qurator-spk/eynollah/actions/workflows/build-docker.yml/badge.svg)](https://github.com/qurator-spk/eynollah/actions/workflows/build-docker.yml)\n[![License: ASL](https://img.shields.io/github/license/qurator-spk/eynollah)](https://opensource.org/license/apache-2-0/)\n[![DOI](https://img.shields.io/badge/DOI-10.1145%2F3604951.3605513-red)](https://doi.org/10.1145/3604951.3605513)\n\n![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg)\n\n## Features\n* Support for 10 distinct segmentation classes: \n  * 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)\n* Support for various image optimization operations:\n  * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing\n* Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text\n* Text recognition (OCR) using either CNN-RNN or Transformer models\n* Detection of reading order (left-to-right or right-to-left) using either heuristics or trainable models\n* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)\n* [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface\n\n:warning: Development is focused on achieving the best quality of results for a wide variety of historical \ndocuments and therefore processing can be very slow. We aim to improve this, but contributions are welcome.\n\n## Installation\n\nPython `3.8-3.11` with Tensorflow `\u003c2.13` on Linux are currently supported.\n\nFor (limited) GPU support the CUDA toolkit needs to be installed. A known working config is CUDA `11` with cuDNN `8.6`.\n\nYou can either install from PyPI\n\n```\npip install eynollah\n```\n\nor clone the repository, enter it and install (editable) with\n\n```\ngit clone git@github.com:qurator-spk/eynollah.git\ncd eynollah; pip install -e .\n```\n\nAlternatively, you can run `make install` or `make install-dev` for editable installation.\n\nTo also install the dependencies for the OCR engines:\n\n```\npip install \"eynollah[OCR]\"\n# or\nmake install EXTRAS=OCR\n```\n\n## Models\n\nPretrained models can be downloaded from [zenodo](https://zenodo.org/records/17194824) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). \n\nFor documentation on models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md). \nModel cards are also provided for our trained models.\n\n## Training\n\nIn case you want to train your own model with Eynollah, see the\ndocumentation in [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md) and use the\ntools in the [`train` folder](https://github.com/qurator-spk/eynollah/tree/main/train).\n\n## Usage\n\nEynollah supports five use cases: layout analysis (segmentation), binarization,\nimage enhancement, text recognition (OCR), and reading order detection.\n\n### Layout Analysis\n\nThe layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading \norder using either heuristic methods or a [pretrained reading order detection model](https://github.com/qurator-spk/eynollah#machine-based-reading-order). \n\nReading order detection can be performed either as part of layout analysis based on image input, or, currently under \ndevelopment, based on pre-existing layout analysis results in PAGE-XML format as input.\n\nThe command-line interface for layout analysis can be called like this:\n\n```sh\neynollah layout \\\n  -i \u003csingle image file\u003e | -di \u003cdirectory containing image files\u003e \\\n  -o \u003coutput directory\u003e \\\n  -m \u003cdirectory containing model files\u003e \\\n     [OPTIONS]\n```\n\nThe following options can be used to further configure the processing:\n\n| option            | description                                                                    |\n|-------------------|:-------------------------------------------------------------------------------|\n| `-fl`             | full layout analysis including all steps and segmentation classes              |\n| `-light`          | lighter and faster but simpler method for main region detection and deskewing  |\n| `-tll`            | this indicates the light textline and should be passed with light version      |\n| `-tab`            | apply table detection                                                          |\n| `-ae`             | apply enhancement (the resulting image is saved to the output directory)       |\n| `-as`             | apply scaling                                                                  |\n| `-cl`             | apply contour detection for curved text lines instead of bounding boxes        |\n| `-ib`             | apply binarization (the resulting image is saved to the output directory)      |\n| `-ep`             | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |\n| `-eoi`            | extract only images to output directory (other processing will not be done)    |\n| `-ho`             | ignore headers for reading order dectection                                    |\n| `-si \u003cdirectory\u003e` | save image regions detected to this directory                                  |\n| `-sd \u003cdirectory\u003e` | save deskewed image to this directory                                          |\n| `-sl \u003cdirectory\u003e` | save layout prediction as plot to this directory                               |\n| `-sp \u003cdirectory\u003e` | save cropped page image to this directory                                      |\n| `-sa \u003cdirectory\u003e` | save all (plot, enhanced/binary image, layout) to this directory               |\n\nIf no further option is set, the tool performs layout detection of main regions (background, text, images, separators \nand marginals).\nThe best output quality is achieved when RGB images are used as input rather than greyscale or binarized images.\n\n### Binarization\n\nThe binarization module performs document image binarization using pretrained pixelwise segmentation models. \n\nThe command-line interface for binarization can be called like this:\n\n```sh\neynollah binarization \\\n  -i \u003csingle image file\u003e | -di \u003cdirectory containing image files\u003e \\\n  -o \u003coutput directory\u003e \\\n  -m \u003cdirectory containing model files\u003e \\\n```\n\n### OCR\n\nThe OCR module performs text recognition using either a CNN-RNN model or a Transformer model.\n\nThe command-line interface for OCR can be called like this:\n\n```sh\neynollah ocr \\\n  -i \u003csingle image file\u003e | -di \u003cdirectory containing image files\u003e \\\n  -dx \u003cdirectory of xmls\u003e \\\n  -o \u003coutput directory\u003e \\\n  -m \u003cdirectory containing model files\u003e | --model_name \u003cpath to specific model\u003e \\\n```\n\n### Machine-based-reading-order\n\nThe machine-based reading-order module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.\n\nThe command-line interface for machine based reading order can be called like this:\n\n```sh\neynollah machine-based-reading-order \\\n  -i \u003csingle image file\u003e | -di \u003cdirectory containing image files\u003e \\\n  -xml \u003cxml file name\u003e | -dx \u003cdirectory containing xml files\u003e \\\n  -m \u003cpath to directory containing model files\u003e \\\n  -o \u003coutput directory\u003e \n```\n\n#### Use as OCR-D processor\n\nEynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli),\nformally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json).\n\nIn this case, the source image file group with (preferably) RGB images should be used as input like this:\n\n    ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models eynollah_layout_v0_5_0\n\nIf the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows:\n- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results)\n- existing annotation (and respective `AlternativeImage`s) are partially _ignored_:\n  - previous page frame detection (`cropped` images)\n  - previous derotation (`deskewed` images)\n  - previous thresholding (`binarized` images)\n- if the page-level image nevertheless deviates from the original (`@imageFilename`)\n  (because some other preprocessing step was in effect like `denoised`), then\n  the output PAGE-XML will be based on that as new top-level (`@imageFilename`)\n\n      ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models eynollah_layout_v0_5_0\n\nIn general, it makes more sense to add other workflow steps **after** Eynollah.\n\nThere is also an OCR-D processor for binarization:\n\n    ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09\n\n#### Additional documentation\n\nAdditional documentation is available in the [docs](https://github.com/qurator-spk/eynollah/tree/main/docs) directory.\n\n## How to cite\n\n```bibtex\n@inproceedings{hip23rezanezhad,\n  title     = {Document Layout Analysis with Deep Learning and Heuristics},\n  author    = {Rezanezhad, Vahid and Baierer, Konstantin and Gerber, Mike and Labusch, Kai and Neudecker, Clemens},\n  booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,\n               San José, CA, USA, August 25-26, 2023},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  year      = {2023},\n  pages     = {73--78},\n  url       = {https://doi.org/10.1145/3604951.3605513}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqurator-spk%2Feynollah","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqurator-spk%2Feynollah","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqurator-spk%2Feynollah/lists"}