Gerber, Mike 3f4ec30349 | 5 years ago | |
---|---|---|
data@dcd3643917 | 5 years ago | |
ocrd-bugs | 5 years ago | |
xsd | 5 years ago | |
.gitignore | 5 years ago | |
.gitmodules | 5 years ago | |
.travis.yml | 5 years ago | |
Dockerfile | 5 years ago | |
README-DEV.md | 5 years ago | |
README.md | 5 years ago | |
build | 5 years ago | |
check-FULLTEXT-Page-dimensions-vs-BEST-dimensions.py | 5 years ago | |
my_ocrd_workflow | 5 years ago | |
ocrd_logging.py | 5 years ago | |
ppn2ocr | 5 years ago | |
qurator_data_lib.sh | 5 years ago | |
requirements-ppn2ocr.txt | 5 years ago | |
requirements.txt | 5 years ago | |
run | 5 years ago | |
run-docker-hub | 5 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 means:
export HTTP_PROXY=http://http-proxy.sbb.spk-berlin.de:3128/
export HTTPS_PROXY=$HTTP_PROXY; export http_proxy=$HTTP_PROXY; export https_proxy=$HTTP_PROXY
export no_proxy=localhost,digital.staatsbibliothek-berlin.de,content.staatsbibliothek-berlin.de