@InProceedings{Mar{\c c}alRusi{\~n}ol2014,
author="Mar{\c{c}}al Rusi{\~n}ol
and J. Chazalon
and Jean-Marc Ogier",
title="Normalisation et validation d{\textquoteright}images de documents captur{\'e}es en mobilit{\'e}",
booktitle="Colloque International Francophone sur l{\textquoteright}{\'E}crit et le Document",
year="2014",
pages="109--124",
optkeywords="mobile document image acquisition",
optkeywords="perspective correction",
optkeywords="illumination correction",
optkeywords="quality assessment",
optkeywords="focus measure",
optkeywords="OCR accuracy prediction",
abstract="Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessmentstep relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods.",
optnote="DAG; 601.223; 600.077",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2546), last updated on Thu, 10 Nov 2016 11:50:30 +0100"
}