| data@dcd3643917 | ||
| ocrd-bugs | ||
| xsd | ||
| .gitmodules | ||
| .travis.yml | ||
| build | ||
| Dockerfile | ||
| my_ocrd_workflow | ||
| ocrd_logging.py | ||
| ppn2ocr | ||
| qurator_data_lib.sh | ||
| README-DEV.md | ||
| README.md | ||
| requirements.txt | ||
| run | ||
| run-docker-hub | ||
My OCR-D workflow
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 and possibly the Dockerfile and the requirements.
Goal
Provide a test environment to produce OCR output for historical prints, using OCR-D, especially ocrd_calamari and 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:
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, if ground truth was available:
firefox OCR-D-OCR-CALAMARI-EVAL/OCR-D-OCR-CALAMARI-EVAL_00000024.html