Detect text blocks and OCR poorly scanned PDFs in bulk. Python module available via pip.
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
doc2text
docs/assets/images
tests
.gitignore
.travis.yml
LICENSE.txt
MANIFEST.in
README.rst
install_deps.sh
setup.cfg
setup.py
tox.ini

README.rst

doc2text

https://travis-ci.org/jlsutherland/doc2text.svg?branch=master

Signup for Announcements doc2text Example

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:

pip install doc2text

doc2text relies on the OpenCV, tesseract, and PythonMagick libraries. To execute the quick-install script, which installs OpenCV, tesseract, and PythonMagick:

curl https://raw.githubusercontent.com/jlsutherland/doc2text/master/install_deps.sh | bash

Manual installation

To install OpenCV manually:

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:

sudo apt-get install tesseract-ocr

To install PythonMagick manually:

sudo apt-get install python-pythonmagick

Example usage

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: