Open AccessBi‐level thresholding for binarisation of handwritten and printed documents
Author(s) -
Jennifer Ranjani J.
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0256
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , thresholding , image (mathematics) , binary image , feature extraction , computer vision , feature (linguistics) , euclidean distance , image processing , linguistics , philosophy
Document image 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 Document Image Binarization 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.