Robust Binarization of Stereo and Monocular Document Images Using Percentile Filter

Muhammad Afzal; M. Kraemer; Syed Saqib Bukhari; Faisal Shafait; Thomas Breuel

In: 5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013, Washungton, DC, USA, August 2013. International Workshop on Camera-Based Document Analysis and Recognition (CBDAR-2013), August 23, Washington, DC, USA, Springer, 2013.


Camera captured documents can be a difficult case for standard binarization algorithms. These algorithms are specifically tailored to the requirements of scanned documents which in general have uniform illumination and high resolution with negligible geometric artifacts. Contrary to this, camera captured images generally are low resolution, contain non-uniform illumination and also posses geometric artifacts. The most important artifact is the defocused or blurred text which is the result of the limited depth of field of the general purpose hand-held capturing devices. These artifacts could be reduced with controlled capture with a single camera but it is inevitable for the case of stereo document images even with the orthoparallel camera setup. Existing methods for binarization require tuning for the parameters separately both for the left and the right images of a stereo pair. In this paper, an approach for binarization based on the local adaptive background estimation using percentile filter has been presented. The presented approach works reasonably well under the same set of parameters for both left and right images. It also shows competitive results for monocular images in comparison with standard binarization methods.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence