Binarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

二值化在文档分析和识别（DAR）系统中起着重要作用。在本文中，我们介绍了基于背景估计和能量最小化的ICFHR 2018手写文档图像二值化（H-DIBCO 2018）竞赛中的获胜算法。首先，我们采用数学形态学运算来估计和补偿文档背景。它使用了一个盘形结构元素，其半径是通过基于最小熵的笔划宽度变换来计算的（SWT）。其次，我们在补偿后的文档图像上执行基于拉普拉斯能量的分割。最后，我们执行后处理，以保持文本笔触的连通性并消除孤立的噪音。实验结果表明，在几种公共基准数据集上，该方法优于其他最新技术。