My OCR-D workflow
=================

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WIP. Given a OCR-D workspace with document images in the OCR-D-IMG file group,
this workflow produces:

* Binarized images
* Line segmentation
* OCR text (using Calamari and Tesseract, both with GT4HistOCR models)
* (Given ground truth in OCR-D-GT-PAGE, also an OCR text evaluation report)

If you're interested in the exact processors, versions and parameters, please take a look at the [script](my_ocrd_workflow) and possibly the [Dockerfile](Dockerfile) and the [requirements](requirements.txt).

Goal
----
Provide a **test environment** to produce OCR output for historical prints, using OCR-D, especially [ocrd_calamari](https://github.com/OCR-D/ocrd_calamari) and [sbb_textline_detection](https://github.com/qurator-spk/sbb_textline_detection), including all dependencies in Docker.

How to use
----------
It's easiest to use it as a pre-built container. To run the container on an
example workspace:

~~~
# Download an example workspace
cd /tmp
wget https://qurator-data.de/examples/actevedef_718448162.first-page.zip
unzip actevedef_718448162.first-page.zip

# Run the workflow on it
cd actevedef_718448162.first-page
~/devel/my_ocrd_workflow/run-docker-hub
~~~

### Build the container yourself
To build the container yourself using Docker:
~~~
cd ~/devel/my_ocrd_workflow
./build
~~~
You may then use the script `run` to use your self-built container, analogous to
the example above.

### Viewing results
You may then examine the results using
[PRImA's PAGE Viewer](https://www.primaresearch.org/tools/PAGEViewer):
~~~
java -jar /path/to/JPageViewer.jar --resolve-dir . OCR-D-OCR-CALAMARI/OCR-D-OCR-CALAMARI_00000024.xml
~~~

The workflow also produces OCR evaluation reports using
[dinglehopper](https://github.com/qurator-spk/dinglehopper), if ground truth was
available:
~~~
firefox OCR-D-OCR-CALAMARI-EVAL/OCR-D-OCR-CALAMARI-EVAL_00000024.html
~~~