My OCR-D workflow ================= [![Build Status](https://travis-ci.org/mikegerber/my_ocrd_workflow.svg?branch=master)](https://travis-ci.org/mikegerber/my_ocrd_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](my_ocrd_workflow) and possibly the individual Dockerfiles. Goal ---- Provide a **test environment** to produce OCR output for historical prints, using OCR-D, especially [ocrd_calamari](https://github.com/OCR-D/ocrd_calamari) and [sbb_textline_detection](https://github.com/qurator-spk/sbb_textline_detection), including all dependencies in Docker. How to use ---------- It's easiest to use it as pre-built containers. To run the containers on an example workspace: ~~~ # Update to the latest stable containers (cd ~/devel/my_ocrd_workflow/; ./run-docker-hub-update) # 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 containers yourself To build the containers yourself using Docker: ~~~ cd ~/devel/my_ocrd_workflow ./build ~~~ You may then use the script `run` to use your self-built containers, analogous to the example above. ### Viewing results You may then examine the results using [PRImA's PAGE Viewer](https://www.primaresearch.org/tools/PAGEViewer): ~~~ 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](https://github.com/qurator-spk/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 workspace and METS file with the best images for a given document in the State Library Berlin (SBB)'s digitized collection. 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)): ~~~ pip install -r ~/devel/my_ocrd_workflow/requirements-ppn2ocr.txt ~~~ The document must be specified by its PPN, for example: ~~~ ~/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 properly set up environment variables for the proxy configuration. At SBB, please read `howto/docker-proxy.md` and `howto/proxy-settings-for-shell+python.md` (in qurator's mono-repo). ocrd-workspace-from-images -------------------------- The `ocrd-workspace-from-images` script produces a OCR-D workspace (incl. METS) for the given images. ~~~ ~/devel/my_ocrd_workflow/ocrd-workspace-from-images 0005.png cd workspace-xxxxx # output by the last command ~/devel/my_ocrd_workflow/run-docker-hub ~~~ This produces a workspace from the files and then runs the OCR workflow on it.