diff --git a/README.md b/README.md index 3cfb587..8a2c4a4 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,8 @@ * Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) * [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface -:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow.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. +:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of +historical documents and therefore processing can be very slow.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. ## Installation Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported. @@ -42,7 +43,7 @@ cd eynollah; pip install -e . Alternatively, you can run `make install` or `make install-dev` for editable installation. ## Models -Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). +Pretrained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). For documentation on methods and models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md). @@ -50,13 +51,17 @@ For documentation on methods and models, have a look at [`models.md`](https://gi In case you want to train your own model with Eynollah, have a look at [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md). ## Usage +Eynollah supports four use cases: layout analysis (segmentation), binarization, text recognition (OCR), +and (trainable) reading order detection. -Eynollah has four key use cases: layout analysis, binarization, OCR, and machine-based reading order. +### Layout Analysis +The layout analysis module is responsible for detecting layouts, identifying text lines, and determining reading order +using both heuristic methods or a machine-based reading order detection model. -### Layout -The layout module is responsible for detecting layouts, identifying text lines, and determining reading order using both heuristic methods or a machine-based reading order detection model. It's important to note that this functionality should not be confused with the machine-based-reading-order use case. The latter, still under development, focuses specifically on determining the reading order for a given layout in an XML file. In contrast, layout detection takes an image as input, and after detecting the layout, it can also determine the reading order using a machine-based model. +Note that there are currently two supported ways for reading order detection: either as part of layout analysis based +on image input, or, currently under development, for given layout analysis results based on PAGE-XML data as input. -The command-line interface for layout can be called like this: +The command-line interface for layout analysis can be called like this: ```sh eynollah layout \ @@ -87,18 +92,19 @@ The following options can be used to further configure the processing: | `-sp ` | save cropped page image to this directory | | `-sa ` | save all (plot, enhanced/binary image, layout) to this directory | -If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals). +If no option is set, the tool performs layout detection of main regions (background, text, images, separators +and marginals). The best output quality is produced when RGB images are used as input rather than greyscale or binarized images. ### Binarization -Document Image Binarization +The binarization module performs document image binarization using pretrained pixelwise segmentation models. The command-line interface for binarization of single image can be called like this: ```sh eynollah binarization \ - -m \ - \ + -m \ + \ ``` @@ -117,9 +123,7 @@ Under development ### Machine-based-reading-order Under development - #### Use as OCR-D processor - Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli), formally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json). @@ -127,7 +131,6 @@ In this case, the source image file group with (preferably) RGB images should be ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models 2022-04-05 - If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows: - existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results) - existing annotation (and respective `AlternativeImage`s) are partially _ignored_: @@ -138,7 +141,6 @@ If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynol (because some other preprocessing step was in effect like `denoised`), then the output PAGE-XML will be based on that as new top-level (`@imageFilename`) - ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models 2022-04-05 Still, in general, it makes more sense to add other workflow steps **after** Eynollah.