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 container. To build the container using Docker:

cd ~/devel/my_ocrd_workflow
./build

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

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