mirror of
https://github.com/qurator-spk/ocrd-galley.git
synced 2025-07-16 00:09:53 +02:00
Mike Gerber
db63b46e65
Installing the ppn2ocr requirements fails when pip is not updated (see issue below), so mention the issue in the README. Quoting https://github.com/skvark/opencv-python#frequently-asked-questions: Q: Pip install fails with ModuleNotFoundError: No module named 'skbuild'? Since opencv-python version 4.3.0.*, manylinux1 wheels were replaced by manylinux2014 wheels. If your pip is too old, it will try to use the new source distribution introduced in 4.3.0.38 to manually build OpenCV because it does not know how to install manylinux2014 wheels. However, source build will also fail because of too old pip because it does not understand build dependencies in pyproject.toml. To use the new manylinux2014 pre-built wheels (or to build from source), your pip version must be >= 19.3. Please upgrade pip with pip install --upgrade pip.
90 lines
2.9 KiB
Markdown
90 lines
2.9 KiB
Markdown
My OCR-D workflow
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=================
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[](https://travis-ci.org/mikegerber/my_ocrd_workflow)
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WIP. Given a OCR-D workspace with document images in the OCR-D-IMG file group,
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this workflow produces:
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* Binarized images
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* Line segmentation
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* OCR text (using Calamari and Tesseract, both with GT4HistOCR models)
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* (Given ground truth in OCR-D-GT-PAGE, also an OCR text evaluation report)
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If you're interested in the exact processors, versions and parameters, please
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take a look at the [script](my_ocrd_workflow) and possibly the individual
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Dockerfiles.
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Goal
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----
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Provide a **test environment** to produce OCR output for historical prints,
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using OCR-D, especially [ocrd_calamari](https://github.com/OCR-D/ocrd_calamari)
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and
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[sbb_textline_detection](https://github.com/qurator-spk/sbb_textline_detection),
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including all dependencies in Docker.
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How to use
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----------
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It's easiest to use it as pre-built containers. To run the containers on an
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example workspace:
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~~~
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# Download an example workspace
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cd /tmp
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wget https://qurator-data.de/examples/actevedef_718448162.first-page.zip
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unzip actevedef_718448162.first-page.zip
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# Run the workflow on it
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cd actevedef_718448162.first-page
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~/devel/my_ocrd_workflow/run-docker-hub
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~~~
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### Build the containers yourself
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To build the containers yourself using Docker:
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~~~
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cd ~/devel/my_ocrd_workflow
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./build
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~~~
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You may then use the script `run` to use your self-built containers, analogous to
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the example above.
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### Viewing results
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You may then examine the results using
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[PRImA's PAGE Viewer](https://www.primaresearch.org/tools/PAGEViewer):
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~~~
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java -jar /path/to/JPageViewer.jar \
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--resolve-dir . \
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OCR-D-OCR-CALAMARI/OCR-D-OCR-CALAMARI_00000024.xml
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~~~
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The workflow also produces OCR evaluation reports using
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[dinglehopper](https://github.com/qurator-spk/dinglehopper), if ground truth was
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available:
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~~~
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firefox OCR-D-OCR-CALAMARI-EVAL/OCR-D-OCR-CALAMARI-EVAL_00000024.html
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~~~
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ppn2ocr
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-------
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The `ppn2ocr` script produces a METS file with the best images for a given
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document in the State Library Berlin (SBB)'s digitized collection.
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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)):
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~~~
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pip install -r ~/devel/my_ocrd_workflow/requirements-ppn2ocr.txt
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~~~
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The document must be specified by its PPN, for example:
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~~~
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~/devel/my_ocrd_workflow/ppn2ocr PPN77164308X
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cd PPN77164308X
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~/devel/my_ocrd_workflow/run-docker-hub -I BEST --skip-validation
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~~~
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This produces a workspace directory `PPN77164308X` with the OCR results in it;
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the results are viewable as explained above.
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ppn2ocr requires a working Docker setup and properly set up environment
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variables for the proxy configuration. At SBB, please read
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`howto/docker-proxy.md` and `howto/proxy-settings-for-shell+python.md`
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(in qurator's mono-repo).
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