Description
===========
.. image:: https://github.com/mittagessen/kraken/actions/workflows/test.yml/badge.svg
:target: https://github.com/mittagessen/kraken/actions/workflows/test.yml
kraken is a turn-key OCR system optimized for historical and non-Latin script
material. This fork is meant to support the EU-funded Digital-to-Distant Diplomatics project.
kraken's main features are:
- Fully trainable layout analysis, reading order, and character recognition
- `Right-to-Left <https://en.wikipedia.org/wiki/Right-to-left>`_, `BiDi
<https://en.wikipedia.org/wiki/Bi-directional_text>`_, and Top-to-Bottom
script support
- `ALTO <https://www.loc.gov/standards/alto/>`_, PageXML, abbyyXML, and hOCR
output
- Word bounding boxes and character cuts
- Multi-script recognition support
- `Public repository <https://zenodo.org/communities/ocr_models>`_ of model files
- Variable recognition network architecture
DiDip changes:
- custom functions for containers
- fixing dependencies (eg. python-bidi)
- ...
Installation
============
kraken only runs on **Linux or Mac OS X**. Windows is not supported.
The latest stable releases can be installed either from `PyPi <https://pypi.org>`_:
::
$ pip install kraken_didip
Finally you'll have to scrounge up a model to do the actual recognition of
characters. To download the default model for printed French text and place it
in the kraken directory for the current user:
::
$ kraken get 10.5281/zenodo.10592716
A list of libre models available in the central repository can be retrieved by
running:
::
$ kraken list
Quickstart
==========
Recognizing text on an image using the default parameters including the
prerequisite steps of binarization and page segmentation:
::
$ kraken -i image.tif image.txt binarize segment ocr
To binarize a single image using the nlbin algorithm:
::
$ kraken -i image.tif bw.png binarize
To segment an image (binarized or not) with the new baseline segmenter:
::
$ kraken -i image.tif lines.json segment -bl
To segment and OCR an image using the default model(s):
::
$ kraken -i image.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel
All subcommands and options are documented. Use the ``help`` option to get more
information.
Documentation
=============
Have a look at the `docs <https://kraken.re>`_.
Related Software
================
These days kraken is quite closely linked to the `eScriptorium
<https://gitlab.com/scripta/escriptorium/>`_ project developed in the same eScripta research
group. eScriptorium provides a user-friendly interface for annotating data,
training models, and inference (but also much more). There is a `gitter channel
<https://gitter.im/escripta/escriptorium>`_ that is mostly intended for
coordinating technical development but is also a spot to find people with
experience on applying kraken on a wide variety of material.
Funding
=======
kraken is developed at the `École Pratique des Hautes Études <https://www.ephe.psl.eu>`_, `Université PSL <https://www.psl.eu>`_.
.. container:: twocol
.. container::
.. image:: https://raw.githubusercontent.com/mittagessen/kraken/main/docs/_static/normal-reproduction-low-resolution.jpg
:width: 100
:alt: Co-financed by the European Union
.. container::
This project was partially funded through the RESILIENCE project, funded from
the European Union’s Horizon 2020 Framework Programme for Research and
Innovation.
.. container:: twocol
.. container::
.. image:: https://projet.biblissima.fr/sites/default/files/2021-11/biblissima-baseline-sombre-ia.png
:width: 400
:alt: Received funding from the Programme d’investissements d’Avenir
.. container::
Ce travail a bénéficié d’une aide de l’État gérée par l’Agence Nationale de la
Recherche au titre du Programme d’Investissements d’Avenir portant la référence
ANR-21-ESRE-0005 (Biblissima+).
Raw data
{
"_id": null,
"home_page": "https://github.com/Didip-eu/kraken_didip",
"name": "kraken-didip",
"maintainer": "Nicolas Renet",
"docs_url": null,
"requires_python": "<=3.11.99,>=3.8",
"maintainer_email": "nicolas.renet@uni-graz.at",
"keywords": "ocr, htr",
"author": "Benjamin Kiessling",
"author_email": "mittagessen@l.unchti.me",
"download_url": "https://files.pythonhosted.org/packages/ac/af/cfed1cd6e20d2654839416dc3bedc349ca96bb0a151bd12f4c2fd65aed17/kraken_didip-0.0.1.dev2116.tar.gz",
"platform": null,
"description": "Description\n===========\n\n.. image:: https://github.com/mittagessen/kraken/actions/workflows/test.yml/badge.svg\n :target: https://github.com/mittagessen/kraken/actions/workflows/test.yml\n\nkraken is a turn-key OCR system optimized for historical and non-Latin script\nmaterial. This fork is meant to support the EU-funded Digital-to-Distant Diplomatics project.\n\nkraken's main features are:\n\n - Fully trainable layout analysis, reading order, and character recognition\n - `Right-to-Left <https://en.wikipedia.org/wiki/Right-to-left>`_, `BiDi\n <https://en.wikipedia.org/wiki/Bi-directional_text>`_, and Top-to-Bottom\n script support\n - `ALTO <https://www.loc.gov/standards/alto/>`_, PageXML, abbyyXML, and hOCR\n output\n - Word bounding boxes and character cuts\n - Multi-script recognition support\n - `Public repository <https://zenodo.org/communities/ocr_models>`_ of model files\n - Variable recognition network architecture\n\nDiDip changes:\n\n - custom functions for containers\n - fixing dependencies (eg. python-bidi)\n - ...\n\nInstallation\n============\n\nkraken only runs on **Linux or Mac OS X**. Windows is not supported.\n\nThe latest stable releases can be installed either from `PyPi <https://pypi.org>`_:\n\n::\n\n $ pip install kraken_didip\n\nFinally you'll have to scrounge up a model to do the actual recognition of\ncharacters. To download the default model for printed French text and place it\nin the kraken directory for the current user:\n\n::\n\n $ kraken get 10.5281/zenodo.10592716\n\nA list of libre models available in the central repository can be retrieved by\nrunning:\n\n::\n\n $ kraken list\n\nQuickstart\n==========\n\nRecognizing text on an image using the default parameters including the\nprerequisite steps of binarization and page segmentation:\n\n::\n\n $ kraken -i image.tif image.txt binarize segment ocr\n\nTo binarize a single image using the nlbin algorithm:\n\n::\n\n $ kraken -i image.tif bw.png binarize\n\nTo segment an image (binarized or not) with the new baseline segmenter:\n\n::\n\n $ kraken -i image.tif lines.json segment -bl\n\n\nTo segment and OCR an image using the default model(s):\n\n::\n\n $ kraken -i image.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel\n\nAll subcommands and options are documented. Use the ``help`` option to get more\ninformation.\n\nDocumentation\n=============\n\nHave a look at the `docs <https://kraken.re>`_.\n\nRelated Software\n================\n\nThese days kraken is quite closely linked to the `eScriptorium\n<https://gitlab.com/scripta/escriptorium/>`_ project developed in the same eScripta research\ngroup. eScriptorium provides a user-friendly interface for annotating data,\ntraining models, and inference (but also much more). There is a `gitter channel\n<https://gitter.im/escripta/escriptorium>`_ that is mostly intended for\ncoordinating technical development but is also a spot to find people with\nexperience on applying kraken on a wide variety of material.\n\nFunding\n=======\n\nkraken is developed at the `\u00c9cole Pratique des Hautes \u00c9tudes <https://www.ephe.psl.eu>`_, `Universit\u00e9 PSL <https://www.psl.eu>`_.\n\n.. container:: twocol\n\n .. container::\n\n .. image:: https://raw.githubusercontent.com/mittagessen/kraken/main/docs/_static/normal-reproduction-low-resolution.jpg\n :width: 100\n :alt: Co-financed by the European Union\n\n .. container::\n\n This project was partially funded through the RESILIENCE project, funded from\n the European Union\u2019s Horizon 2020 Framework Programme for Research and\n Innovation.\n\n\n.. container:: twocol\n\n .. container::\n\n .. image:: https://projet.biblissima.fr/sites/default/files/2021-11/biblissima-baseline-sombre-ia.png\n :width: 400\n :alt: Received funding from the Programme d\u2019investissements d\u2019Avenir\n\n .. container::\n\n Ce travail a b\u00e9n\u00e9fici\u00e9 d\u2019une aide de l\u2019\u00c9tat g\u00e9r\u00e9e par l\u2019Agence Nationale de la\n Recherche au titre du Programme d\u2019Investissements d\u2019Avenir portant la r\u00e9f\u00e9rence\n ANR-21-ESRE-0005 (Biblissima+).\n\n\n",
"bugtrack_url": null,
"license": "Apache",
"summary": "OCR/HTR engine for all the languages: a fork for the DiDip project.",
"version": "0.0.1.dev2116",
"project_urls": {
"Homepage": "https://github.com/Didip-eu/kraken_didip"
},
"split_keywords": [
"ocr",
" htr"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "79d4bda9d274e5ac63a55cd11f913cadb436f76fb3bf2f30bed0fc1973d94823",
"md5": "f686ab97c9187f49d98bd465fda5553c",
"sha256": "221bd75fbc27b177fd5c6d757b1d47f8ff8f3b1d06215be6bbd062c9ac48cd9b"
},
"downloads": -1,
"filename": "kraken_didip-0.0.1.dev2116-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f686ab97c9187f49d98bd465fda5553c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<=3.11.99,>=3.8",
"size": 4980261,
"upload_time": "2024-07-31T06:25:38",
"upload_time_iso_8601": "2024-07-31T06:25:38.700840Z",
"url": "https://files.pythonhosted.org/packages/79/d4/bda9d274e5ac63a55cd11f913cadb436f76fb3bf2f30bed0fc1973d94823/kraken_didip-0.0.1.dev2116-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "acafcfed1cd6e20d2654839416dc3bedc349ca96bb0a151bd12f4c2fd65aed17",
"md5": "c6b93d52695e80c650f1604e7837b6c2",
"sha256": "d043192507a81a8a7d71995b08201971274d949ae161211e957cdc21bd281bf8"
},
"downloads": -1,
"filename": "kraken_didip-0.0.1.dev2116.tar.gz",
"has_sig": false,
"md5_digest": "c6b93d52695e80c650f1604e7837b6c2",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<=3.11.99,>=3.8",
"size": 12834202,
"upload_time": "2024-07-31T06:25:42",
"upload_time_iso_8601": "2024-07-31T06:25:42.262438Z",
"url": "https://files.pythonhosted.org/packages/ac/af/cfed1cd6e20d2654839416dc3bedc349ca96bb0a151bd12f4c2fd65aed17/kraken_didip-0.0.1.dev2116.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-07-31 06:25:42",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Didip-eu",
"github_project": "kraken_didip",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "kraken-didip"
}
JSON
