Markov random field based binarization for hand-held devices captured document images
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
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This paper proposes a document image binarization method, which is especially robust to the images degraded by uneven light condition, such as the camera captured document images. A descriptor that captures the regional properties around a given pixel is first defined for this purpose. For each pixel, the descriptor is defined as a vector composed of filter responses with varying length. This descriptor is shown to give highly discriminating pattern with respect to the background region, text region, and near text region. Of course there are misclassified pixels, which are then relabeled using an energy optimization method, specifically by using the graph cut method. For this, we devise an appropriate energy function that leads to clear and correct binarization. The proposed descriptor is also used for the skew detection, and thus correcting the skewed documents.