Recognize text using Calamari OCR.
ocrd_calamari offers a OCR-D compliant workspace processor for the functionality of Calamari OCR. It uses OCR-D workspaces (METS) with PAGE XML documents as input and output.
This processor only operates on the text line level and so needs a line segmentation (and by extension a binarized image) as its input.
In addition to the line text it may also output word and glyph segmentation
including per-glyph confidence values and per-glyph alternative predictions as
provided by the Calamari OCR engine, using a textequiv_level of word or
glyph. Note that while Calamari does not provide word segmentation, this
processor produces word segmentation inferred from text
segmentation and the glyph positions. The provided glyph and word segmentation
can be used for text extraction and highlighting, but is probably not useful for
further image-based processing.
pip install ocrd_calamaripip install .Download models trained on GT4HistOCR data:
make qurator-gt4histocr-1.0
ls .local/share/ocrd-resources/ocrd-calamari-recognize/*
Manual download: model.tar.xz
Before using ocrd-calamari-recognize get some example data and model:
# Download model and example data
make qurator-gt4histocr-1.0
make example
The example already contains a binarized and line-segmented page, so we are ready to go. Recognize the text using ocrd_calamari and the downloaded model:
cd actevedef_718448162.first-page+binarization+segmentation
ocrd-calamari-recognize \
-P checkpoint_dir qurator-gt4histocr-1.0 \
-I OCR-D-SEG-LINE-SBB -O OCR-D-OCR-CALAMARI
You may want to have a look at the ocrd-tool.json descriptions for additional parameters and default values.
For information regarding development and testing, please see README-DEV.md.