Binarization of degraded handwritten documents based on morphological contrast intensification

Degraded handwritten document images pose several challenges such as faint characters, bleeding-through and large background ink stains for binarization. Traditional binarization techniques fail to handle all these degradations and related problems efficiently. In this paper we present a hybrid binarization technique based on morphological contrast intensification to set up a global threshold for segmentation of candidate text regions from the degraded document images. The proposed approach uses grayscale morphological tools to estimate the background of the image. Using the estimated background information the contrast of the text regions of the document is increased. The histogram of the contrast image is analyzed to obtain a threshold value for initial segmentation of text regions. Finally, the local threshold technique is used to get the final binarized image. The efficacy and accuracy of the proposed technique are also compared, using DIBCO (Document Image Binarization Contest) test dataset (2010, 2011, 2012 and 2013) and evaluation parameters, with other algorithms already reported in the literature.