论文标题
无监督的前景背景分离文档图像的置信得分
Confidence Score for Unsupervised Foreground Background Separation of Document Images
论文作者
论文摘要
前景背景分离是文档图像分析中的重要问题。流行的无监督的二进制方法（例如沙文奴的算法）采用适应性阈值来将像素分类为前景或背景。在这项工作中，我们提出了一种新的方法来计算此类算法中分类的置信度得分。该分数提供了预测置信度的见解。所提出的方法的计算复杂性与基础二进制算法相同。我们的实验说明了在各种应用程序中提出的分数的实用性，例如文档二进制化，文档图像清理和纹理添加。
Foreground-background separation is an important problem in document image analysis. Popular unsupervised binarization methods (such as the Sauvola's algorithm) employ adaptive thresholding to classify pixels as foreground or background. In this work, we propose a novel approach for computing confidence scores of the classification in such algorithms. This score provides an insight of the confidence level of the prediction. The computational complexity of the proposed approach is the same as the underlying binarization algorithm. Our experiments illustrate the utility of the proposed scores in various applications like document binarization, document image cleanup, and texture addition.
