Gerber, Mike 92391747a7 | 4 years ago | |
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ocrd-bugs | 5 years ago | |
.gitignore | 5 years ago | |
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.travis.yml | 4 years ago | |
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Dockerfile-boxed-dinglehopper | 4 years ago | |
Dockerfile-boxed-ocrd_calamari | 4 years ago | |
Dockerfile-boxed-ocrd_olena | 4 years ago | |
Dockerfile-boxed-ocrd_tesserocr | 4 years ago | |
Dockerfile-boxed-sbb_textline_detector | 4 years ago | |
README-DEV.md | 5 years ago | |
README.md | 4 years ago | |
build | 4 years ago | |
my_ocrd_workflow | 4 years ago | |
ocrd_logging.py | 5 years ago | |
ppn2ocr | 5 years ago | |
qurator_data_lib.sh | 4 years ago | |
requirements-ppn2ocr.txt | 5 years ago | |
run | 4 years ago | |
zdb2ocr | 5 years ago |
README.md
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
ppn2ocr
The ppn2ocr
script produces a METS file with the best images for a given
document in the State Library Berlin (SBB)'s digitized collection. The document
must be specified by its PPN, for example:
pip install -r ~/devel/my_ocrd_workflow/requirements-ppn2ocr.txt
~/devel/my_ocrd_workflow/ppn2ocr PPN77164308X
cd PPN77164308X
~/devel/my_ocrd_workflow/run-docker-hub -I BEST --skip-validation
This produces a workspace directory PPN77164308X
with the OCR results in it;
the results are viewable as explained above.
ppn2ocr requires a working Docker setup and properly set up environment
variables for the proxy configuration. At SBB, this following
howto/docker-proxy.md
and howto/proxy-settings-for-shell+python.md
(in qurator's mono-repo).