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Workflow Guide text recognition

Konstantin Baierer edited this page Mar 22, 2021 · 5 revisions

This processor recognizes text in segmented lines.

An overview on the existing model repositories and short descriptions on the most important models can be found here.

We strongly recommend to use the OCR-D resource manager to download the models, as this way you don't have to specify the path to each model.

Available processors

Processor Parameter Remarks Call
ocrd-tesserocr-recognize -P model GT4HistOCR_50000000.997_191951 Recommended
Model can be found here
a faster variant is here
TESSDATA_PREFIX="/test/data/tesseractmodels/" ocrd-tesserocr-recognize -I OCR-D-DEWARP-LINE -O OCR-D-OCR -P model Fraktur+Latin
ocrd-calamari-recognize if you downloaded your model with the [OCR-D resource manager](https://ocr-d.de/en/models), use-P checkpoint_dir modelname
else use -P checkpoint_dir /path/to/models
Recommended
Model can be found here;
For checkpoint you need to pass the local path on your hard drive as parameter value, and keep the verbatim asterisk (*).
ocrd-calamari-recognize -I OCR-D-DEWARP-LINE -O OCR-D-OCR -P checkpoint_dir qurator-gt4histocr-1.0

Note: For ocrd-tesserocr the environment variable TESSDATA_PREFIX has to be set to point to the directory where the used models are stored unless the default directory (normally $VIRTUAL_ENV/share/tessdata) is used. The directory should at least contain the following models: deu.traineddata, eng.traineddata, osd.traineddata.

Note: Faster models for tesserocr-recognize are available from https://ub-backup.bib.uni-mannheim.de/~stweil/ocrd-train/data/Fraktur_5000000/tessdata_fast/. A good and currently the fastest model is Fraktur-fast. UB Mannheim provides many more models online which were trained on different GT data sets, for example from Austrian Newspapers.

Note: If you want to go on with the optional post correction, you should also set the textequiv_level to glyph or in the case of ocrd-calamari-recognize at least word (which is already the default for ocrd-tesserocr-recognize).

Notes on parameter usage

E.g.

  • which parameters do you use with what values?
  • which parameters are insufficiently documented?
  • which aspects of a processor should be parameterizable but are not?

Notes on document-specific usage

E.g. which processors worked best with what material? -- feel free to post sample images here, too.

Welcome to the OCR-D wiki, a companion to the OCR-D website.

Articles and tutorials
Discussions
Expert section on OCR-D- workflows
Particular workflow steps
Recommended workflows
Workflow Guide
Videos
Section on Ground Truth
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