Nonsubsampled contourlet transform and k-means clustering for degraded document image binarization

Abstract

Binarization is the starting step of document analysis and recognition systems. A binarization method is proposed for a degraded historical document image. The binarization methodology is based on the joint use of nonsubsampled contourlet transform (NSCT) for enhancement and k -means clustering for binarization. The input degraded image is decomposed by NSCT for generating coefficients, which are handled through a weighting scheme for highlighting significant features. The resulting reconstructed enhanced image is then binarized by mapping pixels into foreground (text) or background (no text) using k -means clustering. Experiments are conducted on document image binarization competition datasets using blind and unblind evaluation protocol. Unblind evaluation is performed on four specific types of degradations, which are stain, ink bleed-through, nonuniform background, and ink intensity variation. The obtained results show the effectiveness of the proposed scheme in terms of objective and subjective evaluations as well as stability with respect to the other well-known methods.