Curvelet based thinning algorithm

Abstract

Efficiency of a recognition algorithm depends upon availability of noiseless and classes of images with unique and differentiable features. Thinning plays an important part in building a recognition system for various types of images. It is mainly used in handwritten character recognition systems and Biometric systems. In Handwritten character recognition systems where there exist large variations within same class of images, thinning plays a vital role. Here curvelet based thinning algorithm is proposed which produces better results compared to other thinning algorithms. The capability of curvelet transform in estimation of directional features added to the strength of proposed work. Curvelet transform is combined with watershed algorithm, binarization and skeletonization to produce the final thinned output. The efficiency of the proposed work has been estimated in this work through detailed experimental analysis and comparisons with 7 efficient thinning algorithms. The measures used for comparison in this works are number of foreground pixels, PSNR (peak signal to noise ratio), MSE (mean square error), RMSE (root mean square error), reduction rate and thinness ratio.