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

Mike Gerber 2c54a265a6 ✔ Fix tests to use the new filenames OCR-D seems to create the files in the output file group with a new naming scheme (based on file group name and page id); Adjust the tests accordingly. It's probably not the best approach to expect certain filenames, and it would be better to use METS/the OCR-D API to ask for a certain page and it's file, but for now we use this.		2 years ago
.circleci	✔️ CircleCI: Use a test matrix to test on different Python versions	3 years ago
.idea	🔧 Add PyCharm project files	5 years ago
ocrd_calamari	📦 v1.0.5	2 years ago
test	✔ Fix tests to use the new filenames	2 years ago
.coveragerc	✅ Only do the coverage on our code	5 years ago
.gitignore	fix typos	4 years ago
Dockerfile	🐳 Docker: Run pip3 check for good measure	5 years ago
LICENSE	📄 Update license (Fixes #35 )	5 years ago
Makefile	✔️ Move installing deps to Makefile	3 years ago
README-DEV.md	📝 README-DEV: Update wrt ocrd-tool.json and CI	2 years ago
README.md	🧹 README: Do not mention obsolete + unsupported checkpoint parameter	2 years ago
ocrd-tool.json	.	7 years ago
requirements-test.txt	✅ Use GT segmentation to test	5 years ago
requirements.txt	🐛 Fix NumPy dependency (hopefully...)	2 years ago
setup.py	setup.py: read version from ocrd-tool.json	4 years ago

README.md

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 Repo

pip install .

Install models

Download models trained on GT4HistOCR data:

make gt4histocr-calamari1
ls gt4histocr-calamari1

Manual download: model.tar.xz

Example Usage

Before using ocrd-calamari-recognize get some example data and model, and prepare the document for OCR:

# Download model and example data
make gt4histocr-calamari1
make actevedef_718448162

# Create binarized images and line segmentation using other OCR-D projects
cd actevedef_718448162
ocrd-olena-binarize -P impl sauvola-ms-split -I OCR-D-IMG -O OCR-D-IMG-BIN
ocrd-tesserocr-segment-region -I OCR-D-IMG-BIN -O OCR-D-SEG-REGION
ocrd-tesserocr-segment-line -I OCR-D-SEG-REGION -O OCR-D-SEG-LINE

Finally recognize the text using ocrd_calamari and the downloaded model:

ocrd-calamari-recognize -P checkpoint_dir "../gt4histocr-calamari1" -I OCR-D-SEG-LINE -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.