1 research outputs found
Confidence Score for Unsupervised Foreground Background Separation of Document Images
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.Comment: Accepted in Document Analysis Systems (DAS 2022