Image binarization is a preprocessing technique that maps an image’s pixel values to either black or white, and it is crucial in many fields of computer vision, such as document digitization and medical imaging. Thresholding is a popular image binarization technique for grayscale images because it splits pixel values into greater than or lower than a specific threshold. Global thresholding is fast because it computes only one threshold for the entire image, but it cannot handle many types of noise specific to document images. Local thresholding has greater computational complexity because it adjusts the thresholds for each pixel based on the surrounding pixels, but it can handle such types of noise, although it risks introducing noise in uniform areas of the image. Mixed global–local approaches can mitigate this risk while still being able to handle most types of noise. This paper proposes a mixed global–local thresholding method that harnesses two popular automatic machine learning frameworks to train machine learning models using the results of several thresholding algorithms and other image statistics. Cross-validation was performed to ensure that the selected models are robust and perform well on new data. We obtained results comparable with other state-of-the-art methods on popular document image binarization datasets.
Tarbă et al. (Mon,) studied this question.