LRDE Document Binarization Dataset (LRDE DBD)

From TC11
Revision as of 02:23, 31 May 2013 by Liwicki (talk | contribs) (Contact Author)
Jump to: navigation, search

Datasets -> Datasets List -> Current Page

Created: 2013-05-30
Last updated: 2013-005-31

Contact Author

Thierry Géraud –
EPITA Research and Development Laboratory (LRDE)
14-16 rue Voltaire  F-94276 Le Kremlin-Bicetre  France


LRDE is the copyright holder of all the images included in the dataset except for the original documents subset which are copyrighted from Le Nouvel Observateur. This work is based on the French magazine Le Nouvel Observateur, issue 2402, November 18th-24th, 2010.

You are allowed to reuse these documents for research purpose for evaluation and illustration. If so, please specify the following copyright: "Copyright (c) 2012. EPITA Research and Development Laboratory (LRDE) with permission from Le Nouvel Observateur". You are not allowed to redistribute this dataset.

If you use this dataset, please also cite the most appropriate paper from this list:

This data set is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.

Current Version



Document binarization, Magazine, Scanned


This dataset is composed of documents images extracted from the same French magazine: Le Nouvel Observateur, issue 2402, November 18th-24th, 2010.

The provided dataset is composed of 375 Full-Document Images (A4 format, 300-dpi resolution)

  • 125 numerical "original documents" extracted from a PDF, with full OCR groundtruth.
  • 125 numerical "clean documents" created from the "original documents" where images have been removed.
  • 125 "scanned documents" based on the "clean documents". They have been printed, scanned and registered to match the "clean documents".

Purpose of the three document qualities:

  • Original : evaluate the binarization quality on perfect documents mixing text and images.
  • Clean : evaluate the binarization quality on perfect document with text only.
  • Scanned : evaluate the binarization quality on slightly degraded documents with text only.

Ground Truth Data

Related Tasks


  • A setup script is provided to download and configure the benchmarking environment. This is the recommanded way to run this benchmark. Note that this script also includes features to update the dataset if a new version is released.
  • A Python script is provided to launch the benchmark and compute scores.
  • C++ programs (and sources) are provided for performing evaluations and reading ground-truth data.
  • 6 binarization algorithms (and their respective C++ sources) are provided and compiled to run this benchmark on their results.

Minimum requirements: 5GB of free space, Linux (Ubuntu, Debian, …)

Dependencies: Python 2.7, tesseract-ocr, tesseract-ocr-fra, git, libgraphicsmagick++1-dev, graphicsmagick-imagemagick-compat, graphicsmagick-libmagick-dev-compat, build-essential. libtool. automake, autoconf. g++-4.6, libqt4-dev (installed automatically with the setup script on Ubuntu and Debian).


  • G. Lazzara, T. Géraud. Efficient Multiscale Sauvola's Binarization. In International Journal of Document Analysis and Recognition 2013 [[1]]

Submitted Files

Version 1.0

This page is editable only by TC11 Officers .