Improved Thresholding Method for Enhancing Jawi Binarization Performance

Abstract

Most local adaptive image binarization techniques have been inspired by Niblack's method and thus use local thresholding. Niblack's and the NICK methods that is based on it introduce a parameter k to determine the object boundaries in a given window, but one of the deficiencies of local thresholding is that the k value is constant. In this paper, we propose a new approach to calculating k values for the NICK binarization method: we set the k value based on the standard deviation of the image. For our experiments, we used the DIBCO 2013 dataset and a privately held ancient Jawi manuscript dataset. The results were evaluated using the F-measure, pseudo F-measure, peak signal-to-noise ratio, misclassication penalty metrics (MPM), distance reciprocal distortion (DRD), and overall rank score. The proposed method achieved result of 91.09% for the DIBCO dataset and 87.07% for the Jawi dataset, which were higher than those with earlier methods that used fixed k values ranging from - 0.2 to - 0.1. These results indicate that the k values produced by the proposed method can adapt to the state of the manuscript and that using them for NICK thresholding can increase binarization performance.