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        "author": "Abdullah Selek",
        "author_email": "abdullahselek@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "mocr\n======\n\n.. image:: https://img.shields.io/pypi/v/mocr.svg\n    :target: https://pypi.org/pypi/mocr/\n\n.. image:: https://img.shields.io/pypi/pyversions/mocr.svg\n    :target: https://pypi.org/project/mocr\n\n.. image:: https://travis-ci.org/verifid/mocr.svg?branch=master\n    :target: https://travis-ci.org/verifid/mocr\n\n.. image:: https://codecov.io/gh/verifid/mocr/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/verifid/mocr\n\n\nMeaningful Optical Character Recognition from identity cards with Deep Learning.\n\nIntroduction\n============\n\n**mocr** is a library that can be used to detect meaningful optical characters from identity cards. Code base is pure ``Python`` and\nworks with 2.7 and most of the 3.x versions. It has some low level dependencies such as ``Tesseract``. **mocr** uses a pre-trained east\ndetector with OpenCV and applies it's Deep Learning techniques.\n\nIt has a pre-trained east detector inside the module and a custom trained model can be given as a parameter.\n\nPrerequisites\n=============\n\n* `Tessaract <https://github.com/tesseract-ocr/tesseract>`_ must be installed on your computer before using OCR. Please check `installation link <https://github.com/tesseract-ocr/tesseract#installing-tesseract>`_ for details.\n* The other dependencies are listed on ``requirements.txt`` and will be installed when you install with pip.\n\nInstallation\n============\n\n**From source**\n\nInstall module using `pip`::\n\n    $ pip install mocr\n\nDownload the latest `mocr` library from: https://github.com/verifid/mocr\n\nInstall module using `pip`::\n\n    $ pip install -e .\n\nExtract the source distribution and run::\n\n    $ python setup.py build\n    $ python setup.py install\n\nRunning Tests\n=============\n\nThe test suite can be run against a single Python version which requires ``pip install pytest`` and optionally ``pip install pytest-cov`` (these are included if you have installed dependencies from ``requirements.testing.txt``)\n\nTo run the unit tests with a single Python version::\n\n    $ py.test -v\n\nto also run code coverage::\n\n    $ py.test -v --cov-report html --cov=mocr\n\nTo run the unit tests against a set of Python versions::\n\n    $ tox\n\nSample Usage\n============\n\n* ``text_recognition`` Initiating the ``TextRecognizer`` with identity image and then finding the texts with their frames:\n\n.. code:: python\n\n    import os\n    from mocr import TextRecognizer\n\n    image_path = os.path.join('tests', 'data/sample_uk_identity_card.png')\n    east_path = os.path.join('mocr', 'model/frozen_east_text_detection.pb')\n\n    text_recognizer = TextRecognizer(image_path, east_path)\n    (image, _, _) = text_recognizer.load_image()\n    (resized_image, ratio_height, ratio_width, _, _) = text_recognizer.resize_image(image, 320, 320)\n    (scores, geometry) = text_recognizer.geometry_score(east_path, resized_image)\n    boxes = text_recognizer.boxes(scores, geometry)\n    results = text_recognizer.get_results(boxes, image, ratio_height, ratio_width)\n\n    # results: Meaningful texts with bounding boxes\n\n* ``face_detection``:\n\n.. code:: python\n\n    from mocr import face_detection\n\n    image_path = 'YOUR_IDENTITY_IMAGE_PATH'\n    face_image = face_detection.detect_face(image_path)\n    # face_image is the byte array detected and cropped image from original image\n\nCLI\n===\n\nSample command line usage\n\n* Optical Character Recognition\n\n.. code::\n\n    python -m mocr --image tests/data/sample_uk_identity_card.png --east tests/model/frozen_east_text_detection.pb\n\n* Face detection\n\n.. code::\n\n    python -m mocr --image-face 'tests/data/sample_de_identity_card.jpg'\n\nScreenshots\n-----------\n\n**Before**\n\n|image_before|\n\n**After**\n\n|image_after|\n\n.. |image_before| image:: https://raw.githubusercontent.com/verifid/mocr/master/screenshots/sample_uk_identity_card.png\n.. |image_after| image:: https://raw.githubusercontent.com/verifid/mocr/master/screenshots/uk_identity_card_after_detection.png\n\n\n",
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        "keywords": "ocr,optical character recognition,identity card,deep learning,opencv,meaningful",
        "license": "MIT License",
        "maintainer": "Abdullah Selek",
        "maintainer_email": "abdullahselek@gmail.com",
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