doc2text
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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()
# You can pass the lang (as 3 letters code) to the class to improve accuracy
# On ubuntu it requires the package tesseract-ocr-$lang$
# On other OS, see https://github.com/tesseract-ocr/langdata
doc = doc2text.Document(lang="eng")
# 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.