Text line segmentation and binarization of handwritten historical documents using the fast and adaptive bidimensional empirical mode decomposition

Abstract

Text line segmentation is a basic step that can be used to serve several processes in the recognition stage. Image segmentation is thus inevitable. A necessary step for text line segmentation is the image binarization. However, it is challenging due to complex background in such images. In this paper, we present a new text line segmentation and banarization method based on fast and adaptive bidimensional empirical mode decomposition (FABEMD). To achieve this, we use the FABEMD method to decompose each image into two components. The binarization threshold is calculated taking into account the characteristics of each component. The inspiration for the new binarization method was drawn from the standard Niblack's and Sauvola's Algorithms. Then the text line segmentation is performed after eliminating the BIMF1 (bidimensional intrinsic mode functions) which could disrupt the segmentation process. The results show that the proposed approach is less sensitive to noise and provides improved text line segmentation and binarization compared to iterative methods.