ocrd_calamari

Recognize text using Calamari OCR.

Introduction

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.

Installation

From PyPI

pip install ocrd_calamari

From the git repository

pip install .

Install models

Download models trained on GT4HistOCR data:

make qurator-gt4histocr-1.0
ls .local/share/ocrd-resources/ocrd-calamari-recognize/*

Manual download: model.tar.xz

Example Usage

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.

Development & Testing

For information regarding development and testing, please see README-DEV.md.