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120 lines
3.7 KiB
Markdown
120 lines
3.7 KiB
Markdown
ocrd-galley
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===========
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<!--
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[](https://travis-ci.com/qurator-spk/ocrd-galley)
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-->
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A Dockerized test environment for OCR-D processors 🚢
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WIP. Given a OCR-D workspace with document images in the OCR-D-IMG file group,
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the example 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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ocrd-galley uses Docker to run the OCR-D images. We provide pre-built container
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images that get downloaded automatically when you run the provided wrappers for
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the OCR-D processors.
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You can then install the wrappers into a Python venv:
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~~~
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cd ~/devel/ocrd-galley/wrapper
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pip install .
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~~~
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To download models, you need to use the `-a` flag of `ocrd resmgr`:
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~~~
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ocrd resmgr download -a ocrd-calamari-recognize default
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~~~
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You may then use the script `my_ocrd_workflow` to use your self-built
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containers on an 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/ocrd-galley/my_ocrd_workflow
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~~~
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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 example 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 workspace and METS file with the best images for
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a given document in the Berlin State Library (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/ocrd-galley/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/ocrd-galley/ppn2ocr PPN77164308X
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cd PPN77164308X
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~/devel/ocrd-galley/my_ocrd_workflow -I MAX --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 properly set up environment variables for the proxy
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configuration. At SBB, please read `howto/docker-proxy.md` and
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`howto/proxy-settings-for-shell+python.md` (in qurator's mono-repo).
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ocrd-workspace-from-images
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--------------------------
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The `ocrd-workspace-from-images` script produces a OCR-D workspace (incl. METS)
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for the given images.
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~~~
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~/devel/ocrd-galley/ocrd-workspace-from-images 0005.png
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cd workspace-xxxxx # output by the last command
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~/devel/ocrd-galley/my_ocrd_workflow
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~~~
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This produces a workspace from the files and then runs the OCR workflow on it.
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Build the containers yourself
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-----------------------------
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To build the containers yourself using Docker:
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~~~
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cd ~/devel/ocrd-galley/
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./build
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~~~
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