Metadata-Version: 1.1
Name: doc2text
Version: 0.2.2
Summary: doc2text drastically improves the extraction of text from images by fixing resolution, text area (crop), and skew.
Home-page: https://github.com/jlsutherland/doc2text
Author: Joseph L. Sutherland
Author-email: joe@clvr.tech
License: MIT
Description: doc2text
        ========
        
        .. image:: https://travis-ci.org/jlsutherland/doc2text.svg?branch=master
           :target: https://travis-ci.org/jlsutherland/doc2text
        
        .. image:: https://badge.fury.io/py/doc2text.svg
            :target: https://badge.fury.io/py/doc2text
        
        
        `doc2text` extracts higher quality text by fixing common scan errors
        --------------------------------------------------------------------
        Developing text corpora can be a massive pain in the butt. Much of the text data we are interested in as scientists are locked away in pdfs that are poorly scanned. These scans can be off kilter, poor resolution, have a hand in them... and if you OCR these scans without fixing these errors, the OCR doesn't turn out so well. `doc2text` was created to help researchers fix these errors and extract the highest quality text from
        their pdfs as possible.
        
        
        `doc2text` is super duper alpha atm
        -----------------------------------
        `doc2text` is developed and tested on Ubuntu 16.04 LTS Xenial Xerus. We do not pretend to serve all operating systems at the moment because that would be irresponsible. Please use this software with a huge grain of salt. We are currently working on:
        
        - Increasing the responsiveness of the text block identifier.
        - Optimizing the binarization for tesseract detection.
        - Identifying text in multiple columns (right now, treats as one big column).
        - Handling tables.
        - Many other optimizations.
        
        Support and Contributions
        -------------------------
        If you have feedback or would like to contribute, *please, please* submit a pull request or contact me at `joseph dot sutherland at columbia dot edu`.
        
        
        Installation
        ------------
        To install the `doc2text` package, simply:
        
        .. code-block:: python
        
           pip install doc2text
        
        `doc2text` relies on the `OpenCV <http://github.com/opencv/opencv>`_, `tesseract <http://github.com/tesseract-ocr/tesseract>`_, and `PythonMagick` libraries. To execute the quick-install script, which installs OpenCV, tesseract, and PythonMagick:
        
        .. code-block:: bash
        
           curl https://raw.githubusercontent.com/jlsutherland/doc2text/master/install_deps.sh | bash
        
        Manual installation
        ~~~~~~~~~~~~~~~~~~~
        To install OpenCV manually:
        
        .. code-block:: bash
        
           sudo apt-get install -y build-essential
           sudo apt-get install -y cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
           sudo apt-get install -y python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
           git clone https://github.com/opencv/opencv.git opencv
           git clone https://github.com/opencv/opencv_contrib.git opencv_contrib
           cd opencv
           git checkout 3.1.0
           cd ../opencv_contrib
           git checkout 3.1.0
           cd ../opencv
           mkdir build
           cd build
           cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D INSTALL_C_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=ON -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules -D BUILD_EXAMPLES=ON ..
           make -j4
           sudo make install
           sudo ldconfig
        
        To install tesseract manually:
        
        .. code-block:: bash
        
           sudo apt-get install tesseract-ocr
        
        To install PythonMagick manually:
        
        .. code-block:: bash
        
           sudo apt-get install python-pythonmagick
        
        Example usage
        -------------
        
        .. code-block:: python
        
           import doc2text
        
           # Initialize the class.
           doc = doc2text.Document()
        
           # Read the file in. Currently accepts pdf, png, jpg, bmp, tiff.
           # If reading a PDF, doc2text will split the PDF into its component pages.
           doc.read('./path/to/my/file')
        
           # Crop the pages down to estimated text regions, deskew, and optimize for OCR.
           doc.process()
        
           # Extract text from the pages.
           doc.extract_text()
           text = doc.get_text()
        
        Big thanks
        ----------
        
        doc2text would be nothing without the open-source contributions of:
        
        - `@danvk <http://github.com/danvk>`_
        - `@jrosebr1 <http://github.com/jrosebr1>`_
        - Countless stackoverflow posts and comments.
        
Keywords: ocr text detection machine learning computer vision
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
