Historical document image binarization using background estimation and energy minimization

This paper presents an enhanced historical document image binarization technique that makes use of background estimation and energy minimization. Given a degraded historical document image, mathematical morphology is first carried out to compensate the document background with a disk-shaped mask, whose size is determined by the stroke width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.