Historical document image binarization using background estimation and energy minimization

Abstract

This paper presents an enhanced historical document image binarization technique that makes use of background estimation and energy minimization. Given a degraded historical document image, mathematical morphology is first carried out to compensate the document background with a disk-shaped mask, whose size is determined by the stroke width transform (SWT). The Laplacian energy based segmentation is then performed on the enhanced document image. Finally, the post-processing is further applied to improve the binarization results. The proposed technique has been extensively evaluated over the recent DIBCO and H-DIBCO benchmark datasets. Experimental results show that our proposed method outperforms other state-of-the-art document image binarization techniques.