Shape based local thresholding for binarization of document images

Authors:
Jichuan Shi;Nilanjan Ray;Hong Zhang
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

This paper presents a novel local threshold algorithm for the binarization of document images. Stroke width of handwritten and printed characters in documents is utilized as the shape feature. As a result, in addition to the intensity analysis, the proposed algorithm introduces the stroke width as shape information into local thresholding. Experimental results for both synthetic and practical document images show that the proposed local threshold algorithm is superior in terms of segmentation quality to the threshold approaches that solely use intensity information.