readme and documentation updates

README.md

@@ -2,6 +2,7 @@
 
 > Document Layout Analysis, Binarization and OCR with Deep Learning and Heuristics
 
+[![Python Versions](https://img.shields.io/pypi/pyversions/eynollah.svg)](https://pypi.python.org/pypi/eynollah)
 [![PyPI Version](https://img.shields.io/pypi/v/eynollah)](https://pypi.org/project/eynollah/)
 [![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)
 [![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)
@@ -11,24 +12,22 @@
 ![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg)
 
 ## Features
-* Support for 10 distinct segmentation classes: 
+* Document layout analysis using pixelwise segmentation models with support for 10 distinct segmentation classes: 
   * 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)
-* Support for various image optimization operations:
-  * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
 * Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
-* Text recognition (OCR) using either CNN-RNN or Transformer models
+* Document image binarization with pixelwise segmentation or hybrid CNN-Transformer models
-* Detection of reading order (left-to-right or right-to-left) using either heuristics or trainable models
+* Text recognition (OCR) with CNN-RNN or TrOCR models
+* Detection of reading order (left-to-right or right-to-left) using heuristics or trainable models
 * 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 focused on achieving the best 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.
+documents using a combination of multiple deep learning models and heuristics; therefore processing can be slow.
 
 ## Installation
-
 Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported.
-
+For (limited) GPU support the CUDA toolkit needs to be installed. 
-For (limited) GPU support the CUDA toolkit needs to be installed. A known working config is CUDA `11` with cuDNN `8.6`.
+A working config is CUDA `11.8` with cuDNN `8.6`.
 
 You can either install from PyPI
 
@@ -53,23 +52,33 @@ pip install "eynollah[OCR]"
 make install EXTRAS=OCR
 ```
 
+With Docker, use
+
+```
+docker pull ghcr.io/qurator-spk/eynollah:latest
+```
+
+For additional documentation on using Eynollah and Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md).
+
 ## Models
 
-Pretrained models can be downloaded from [zenodo](https://zenodo.org/records/17194824) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). 
+Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah). 
 
 For documentation on models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
-Model cards are also provided for our trained models.
 
 ## Training
 
-In case you want to train your own model with Eynollah, see the
+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
-documentation in [`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.
-tools in the [`train` folder](https://github.com/qurator-spk/eynollah/tree/main/train).
 
 ## Usage
 
-Eynollah supports five use cases: layout analysis (segmentation), binarization,
+Eynollah supports five use cases: 
-image enhancement, text recognition (OCR), and reading order detection.
+1. [layout analysis (segmentation)](#layout-analysis), 
+2. [binarization](#binarization), 
+3. [image enhancement](#image-enhancement), 
+4. [text recognition (OCR)](#ocr), and 
+5. [reading order detection](#reading-order-detection).
 
 ### Layout Analysis
 
@@ -114,6 +123,8 @@ If no further option is set, the tool performs layout detection of main regions
 and marginals).
 The best output quality is achieved when RGB images are used as input rather than greyscale or binarized images.
 
+Additional documentation can be found in [`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
+
 ### Binarization
 
 The binarization module performs document image binarization using pretrained pixelwise segmentation models. 
@@ -127,9 +138,12 @@ eynollah binarization \
   -m <directory containing model files> \
 ```
 
+### Image Enhancement
+TODO
+
 ### OCR
 
-The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
+The OCR module performs text recognition using either CNN-RNN or TrOCR models.
 
 The command-line interface for OCR can be called like this:
 
@@ -141,7 +155,7 @@ eynollah ocr \
   -m <directory containing model files> | --model_name <path to specific model> \
 ```
 
-### Machine-based-reading-order
+### Reading Order Detection
 
 The machine-based reading-order module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
 
@@ -160,36 +174,12 @@ eynollah machine-based-reading-order \
 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).
 
-In this case, the source image file group with (preferably) RGB images should be used as input like this:
+Further documentation on using Eynollah with OCR-D can be found in [`ocrd.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/ocrd.md).
-
-    ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models eynollah_layout_v0_5_0
-
-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_:
-  - previous page frame detection (`cropped` images)
-  - previous derotation (`deskewed` images)
-  - previous thresholding (`binarized` images)
-- if the page-level image nevertheless deviates from the original (`@imageFilename`)
-  (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 eynollah_layout_v0_5_0
-
-In general, it makes more sense to add other workflow steps **after** Eynollah.
-
-There is also an OCR-D processor for binarization:
-
-    ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09
-
-#### Additional documentation
-
-Additional documentation is available in the [docs](https://github.com/qurator-spk/eynollah/tree/main/docs) directory.
 
 ## How to cite
 
 ```bibtex
-@inproceedings{hip23rezanezhad,
+@inproceedings{hip23eynollah,
   title     = {Document Layout Analysis with Deep Learning and Heuristics},
   author    = {Rezanezhad, Vahid and Baierer, Konstantin and Gerber, Mike and Labusch, Kai and Neudecker, Clemens},
   booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,

docs/docker.md

@@ -0,0 +1,24 @@
+# 1. ocrd resource manager
+(just once, to get the models and install them into a named volume for later re-use)
+
+    vol_models=ocrd-resources:/usr/local/share/ocrd-resources
+    docker run --rm -v $vol_models ocrd/eynollah ocrd resmgr download ocrd-eynollah-segment default
+
+Now, each time you want to use Eynollah, pass the same resources volume again.
+Also, bind-mount some data directory, e.g. current working directory $PWD (/data is default working directory in the container).
+Either use standalone CLI (2) or OCR-D CLI (3):
+
+# 2. standalone CLI (follow self-help, cf. readme)
+
+    docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah binarization --help
+    docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah layout --help
+    docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah ocr --help
+
+# 3. OCR-D CLI (follow self-help, cf. readme and https://ocr-d.de/en/spec/cli)
+
+    docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-eynollah-segment -h
+    docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-sbb-binarize -h
+
+Alternatively, just "log in" to the container once and use the commands there:
+
+    docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash

docs/ocrd.md

@@ -0,0 +1,21 @@
+When using Eynollah in OCR-D, the source image file group with (preferably) RGB images should be used as input like this:
+
+    ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models eynollah_layout_v0_5_0
+
+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_:
+  - previous page frame detection (`cropped` images)
+  - previous derotation (`deskewed` images)
+  - previous thresholding (`binarized` images)
+- if the page-level image nevertheless deviates from the original (`@imageFilename`)
+  (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 eynollah_layout_v0_5_0
+
+In general, it makes more sense to add other workflow steps **after** Eynollah.
+
+There is also an OCR-D processor for binarization:
+
+    ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09