Achieving a good recognition rate for degraded documentimages is difficult as degraded documentimages suffer from low contrast,bleedthrough,and nonuniform illumination *** the existing baseline thresholding techniqu...
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Achieving a good recognition rate for degraded documentimages is difficult as degraded documentimages suffer from low contrast,bleedthrough,and nonuniform illumination *** the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better *** enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced *** the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic *** dynamic window is derived by exploiting the advantage of Otsu *** assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for *** comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.
documentimage binarisation algorithms have been available in the literature for decades. However, most of the state-of-the-art methods address specific image degradation or characteristics. Moreover, they require one...
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documentimage binarisation algorithms have been available in the literature for decades. However, most of the state-of-the-art methods address specific image degradation or characteristics. Moreover, they require one or more parameters to be tuned manually so as to present a significant binary image. In this study, a hybrid approach for document binarisation is presented. In the pre-processing stage, the degradation in the background image is smoothed using the L-0-gradient minimisation algorithm and the foreground is enhanced using the local contrast feature. A divide and conquer based recursive auto-thresholding algorithm is then utilised to binarise the enhanced image. The proposed algorithm is evaluated objectively using the evaluation metrics such as F-measure, peak signal-to-noise ratio, negative rate metric. The extensive experiments over the different datasets including the document image binarization contest (DIBCO) 2009, Handwritten documentimagebinarization Competition (H-DIBCO) 2010, DIBCO 2011 and H-DIBCO 2012 show that the proposed hybrid binarisation algorithm outperforms most of the state-of-the-art algorithms significantly.
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