You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
Go to file
Konstantin Baierer 0f47d1fca5 makefile: pip -> pip3 5 years ago
.circleci Use Ubuntu 18.04 LTS again (19.04 is EOL 2020-01) 5 years ago
ocrd_calamari 📝 readme, makefile, synopsis for cli 5 years ago
test 🐛 Open our test result with UTF-8 encoding (for Python 3.6?) 5 years ago
.coveragerc Only do the coverage on our code 5 years ago
.gitignore smoke test, circle ci 5 years ago
Dockerfile Dockerfile 5 years ago
LICENSE Initial commit 6 years ago
Makefile makefile: pip -> pip3 5 years ago
README-DEV.md 📝 Add README-DEV.md with info how to release 5 years ago
README.md 📝 readme: remove misleading paragraph on installing GPU-capable calamari 5 years ago
ocrd-tool.json . 6 years ago
requirements-test.txt Use GT segmentation to test 5 years ago
requirements.txt 🐛 Further tighten dependencies to a known good configuration 5 years ago
setup.py 📦 v0.0.3 – To fix version inconsistency 5 years ago

README.md

ocrd_calamari

Recognize text using Calamari OCR.

image image image

Introduction

This offers a OCR-D compliant workspace processor for some of the functionality of Calamari OCR.

This processor only operates on the text line level and so needs a line segmentation (and by extension a binarized image) as its input.

Installation

From PyPI

pip install ocrd_calamari

From Repo

pip install .

Install models

Download standard models:

wget https://github.com/Calamari-OCR/calamari_models/archive/master.zip
unzip master.zip

Download models trained on GT4HistOCR data:

wget https://file.spk-berlin.de:8443/calamari-models/GT4HistOCR/model.tar.xz
mkdir gt4hist-calamari
cd gt4hist-calamari
tar xf ../model.tar.xz

Example Usage

ocrd-calamari-recognize -p test-parameters.json -m mets.xml -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI

With test-parameters.json:

{
    "checkpoint": "/path/to/some/trained/models/*.ckpt.json"
}

TODO

  • Support Calamari's "extended prediction data" output
  • Currently, the processor only supports a prediction using confidence voting of multiple models. While this is superior, it makes sense to support single model prediction, too.