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README.md
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README.md
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@ -11,23 +11,24 @@
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## Features
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* Support for up to 10 segmentation classes:
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* Support for 10 distinct 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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* Textline 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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* Text recognition (OCR) using either CNN-RNN or Transformer models
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* Detection of reading order (left-to-right or right-to-left) using either heuristics or trainable models
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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
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historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
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:warning: Development is focused on achieving the best quality of results for a wide variety of historical
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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 `<2.13` on Linux are currently supported.
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For (limited) GPU support the CUDA toolkit needs to be installed.
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For (limited) GPU support the CUDA toolkit needs to be installed. A known working config is CUDA `11` with cuDNN `8.6`.
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You can either install from PyPI
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@ -57,25 +58,26 @@ make install EXTRAS=OCR
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Pretrained models can be downloaded from [zenodo](https://zenodo.org/records/17194824) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
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For documentation on methods and models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
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Model cards are also provided for our trained models.
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## Training
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In case you want to train your own model with Eynollah, have see the
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In case you want to train your own model with Eynollah, see the
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documentation in [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md) and use the
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tools in the [`train` folder](https://github.com/qurator-spk/eynollah/tree/main/train).
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## Usage
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Eynollah supports five use cases: layout analysis (segmentation), binarization,
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image enhancement, text recognition (OCR), and (trainable) reading order detection.
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image enhancement, text recognition (OCR), and reading order detection.
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### Layout Analysis
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The layout analysis module is responsible for detecting layouts, identifying text lines, and determining reading order
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using both heuristic methods or a machine-based reading order detection model.
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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 reading order detection model.
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Note that there are currently two supported ways for reading order detection: either as part of layout analysis based
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on image input, or, currently under development, for given layout analysis results based on PAGE-XML data as input.
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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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@ -108,15 +110,15 @@ The following options can be used to further configure the processing:
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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
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If no further option is set, the tool performs layout detection of main regions (background, text, images, separators
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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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The best output quality is achieved when RGB images are used as input rather than greyscale or binarized images.
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### Binarization
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The binarization module performs document image binarization using pretrained pixelwise segmentation models.
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The command-line interface for binarization of single image can be called like this:
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The command-line interface for binarization can be called like this:
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```sh
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eynollah binarization \
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@ -127,16 +129,16 @@ eynollah binarization \
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### OCR
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The OCR module performs text recognition from images using two main families of pretrained models: CNN-RNN–based OCR and Transformer-based OCR.
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The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
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The command-line interface for ocr can be called like this:
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The command-line interface for OCR can be called like this:
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```sh
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eynollah ocr \
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-i <single image file> | -di <directory containing image files> \
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-dx <directory of xmls> \
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-o <output directory> \
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-m <path to directory containing model files> | --model_name <path to specific model> \
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-m <directory containing model files> | --model_name <path to specific model> \
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```
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### Machine-based-reading-order
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@ -174,20 +176,18 @@ If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynol
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ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models eynollah_layout_v0_5_0
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Still, in general, it makes more sense to add other workflow steps **after** Eynollah.
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In general, it makes more sense to add other workflow steps **after** Eynollah.
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There is also an OCR-D processor for the binarization:
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There is also an OCR-D processor for binarization:
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ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09
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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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Additional documentation is available in the [docs](https://github.com/qurator-spk/eynollah/tree/main/docs) directory.
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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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