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        "description": "Overview\n========\n\n.. image:: https://travis-ci.org/OpenPhilology/nidaba.svg\n        :target: https://travis-ci.org/OpenPhilology/nidaba\n\nNidaba is the central controller for the entire OGL OCR pipeline. It oversees\nand automates the process of converting raw images into citable collections of\ndigitized texts.\n\nIt offers the following functionality:\n\n- Grayscale Conversion\n- Binarization utilizing `Sauvola\n  <http://www.mediateam.oulu.fi/publications/pdf/24.p>`__ adaptive\n  thresholding, Otsu, or ocropus's nlbin algorithm\n- Deskewing\n- Dewarping\n- Integration of `tesseract <http://code.google.com/p/tesseract-ocr/>`_,\n  `kraken <http://mittagessen.github.io/kraken>`_, and `ocropus\n  <http://github.com/tmbdev/ocropy>`_ OCR engines\n- Page segmentation from the aforementioned OCR packages\n- Various postprocessing utilities like spell-checking, merging of multiple\n  results, and ground truth comparison.\n\nAs it is designed to use a common storage medium on network attached storage\nand the `celery <http://celeryproject.org>`__ distributed task queue it scales\nnicely to multi-machine clusters.\n\nBuild\n=====\n\nTo easiest way to install the latest stable(-ish) nidaba is from PyPi:\n\n::\n\n    $ pip install nidaba\n\nor run:\n\n::\n\n    $ pip install .\n\nin the git repository for the bleeding edge development version.\n\nSome useful tasks have external dependencies. A good start is:\n\n::\n\n    # apt-get install libtesseract3 tesseract-ocr-eng libleptonica-dev liblept\n\nTests\n=====\n\nPer default no dictionaries and OCR models necessary to runs the tests are\ninstalled. To download the necessary files run:\n\n::\n\n    $ python setup.py download\n\n::\n\n    $ python setup.py nosetests\n\nTests for modules that call external programs, at the time only tesseract,\nocropus, and kraken, will be skipped if these aren't installed.\n\nRunning\n=======\n\nFirst edit (the installed) nidaba.yaml and celery.yaml to fit your needs. Have\na look at the `docs <https:///mittagessen.github.io/nidaba>`__ if you haven't\nset up a celery-based application before.\n\nThen start up the celery daemon with something like:\n\n::\n\n    $ celery -A nidaba worker\n\nNext jobs can be added to the pipeline using the nidaba executable:\n\n::\n\n    $ nidaba batch -b otsu -l tesseract -o tesseract:eng -- ./input.tiff\n    Preparing filestore             [\u2713]\n    Building batch                  [\u2713]\n    951c57e5-f8a0-432d-8d77-8a2e27fff53c\n\nUsing the return code the current state of the job can be retrieved:\n\n::\n\n    $ nidaba status 25d79a54-9d4a-4939-acb6-8e168d6dbc7c\n    PENDING\n\nWhen the job has been processed the status command will return a list of paths\ncontaining the final output:\n\n::\n\n    $ nidaba status 951c57e5-f8a0-432d-8d77-8a2e27fff53c\n    SUCCESS\n    14.tif \u2192 .../input_img.rgb_to_gray_binarize.otsu_ocr.tesseract_grc.tif.hocr\n\nDocumentation\n=============\n\nWant to learn more? `Read the\nDocs <https:///openphilology.github.io/nidaba/>`__\n\n\n\n",
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