ocrd-galley
===========
<!--
[](https://travis-ci.com/qurator-spk/ocrd-galley)
-->
A Dockerized test environment for OCR-D processors 🚢
WIP. Given a OCR-D workspace with document images in the OCR-D-IMG file group,
the example 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 individual
Dockerfiles.
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
----------
**Currently, due to problems with the Travis CI, we do not provide pre-built
containers anymore.***
To build the containers yourself using Docker:
~~~
cd ~/devel/ocrd-galley/
./build
~~~
You can then install the wrappers into a Python venv:
~~~
cd ~/devel/ocrd-galley/wrapper
pip install .
~~~
You may then use the script `my_ocrd_workflow` to use your self-built
containers 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/ocrd-galley/my_ocrd_workflow
~~~
### 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 example 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
~~~
ppn2ocr
-------
The `ppn2ocr` script produces a workspace and METS file with the best images for
a given document in the Berlin State Library (SBB)'s digitized collection.
Install it with an up-to-date pip (otherwise this will fail due to [a opencv-python-headless build failure ](https://github.com/skvark/opencv-python#frequently-asked-questions )):
~~~
pip install -r ~/devel/ocrd-galley/requirements-ppn2ocr.txt
~~~
The document must be specified by its PPN, for example:
~~~
~/devel/ocrd-galley/ppn2ocr PPN77164308X
cd PPN77164308X
~/devel/ocrd-galley/my_ocrd_workflow -I MAX --skip-validation
~~~
This produces a workspace directory `PPN77164308X` with the OCR results in it;
the results are viewable as explained above.
ppn2ocr requires properly set up environment variables for the proxy
configuration. At SBB, please read `howto/docker-proxy.md` and
`howto/proxy-settings-for-shell+python.md` (in qurator's mono-repo).
ocrd-workspace-from-images
--------------------------
The `ocrd-workspace-from-images` script produces a OCR-D workspace (incl. METS)
for the given images.
~~~
~/devel/ocrd-galley/ocrd-workspace-from-images 0005.png
cd workspace-xxxxx # output by the last command
~/devel/ocrd-galley/my_ocrd_workflow
~~~
This produces a workspace from the files and then runs the OCR workflow on it.
