LRDE Document Binarization Dataset (LRDE DBD)
Thierry Géraud – firstname.lastname@example.org EPITA Research and Development Laboratory (LRDE) 14-16 rue Voltaire F-94276 Le Kremlin-Bicetre France
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".
Ground Truth Data
- Ground Truth for LRDE DBD text line localization
- Ground Truth for LRDE DBD binarization
- Ground Truth for LRDE DBD OCR
- 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 []
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