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
Gerber, Mike 1c7fcda767 📝 README: Link to model download 4 years ago
.circleci 🐛 CircleCI: Ignore screenshots branch (second try) 4 years ago
.idea 🔧 Add PyCharm project files 5 years ago
ocrd_calamari 📦 v1.0.0 4 years ago
test Merge branch 'feat/update-calamari1' 4 years ago
.coveragerc Only do the coverage on our code 5 years ago
.gitignore 📝 README: Provide a complete example using real data and other processors 5 years ago
Dockerfile 🐳 Docker: Run pip3 check for good measure 5 years ago
LICENSE 📄 Update license (Fixes #35) 5 years ago
Makefile 👷🏾‍♂️ Use gt4histocr-calamari1/ as directory name for the Calmari 1 model 4 years ago
README-DEV.md 🗒️ README-DEV: Also release on GitHub 4 years ago
README.md 📝 README: Link to model download 4 years ago
ocrd-tool.json . 7 years ago
requirements-test.txt Use GT segmentation to test 5 years ago
requirements.txt 🐛 Pin h5py to < 3 because pip 4 years ago
setup.py 📦 v1.0.0 4 years ago

README.md

ocrd_calamari

Recognize text using Calamari OCR.

image image image

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.

Example output as viewed in PAGE Viewer

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-BINPAGE,OCR-D-IMG-BIN
ocrd-tesserocr-segment-region -I OCR-D-IMG-BINPAGE -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": "../gt4histocr-calamari1/*.ckpt.json" }' -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.