Document image binarization via one-pass local classification

Binarization algorithms are used to create a binary representation of a raster document image, typically with the intent of identifying text and separating it from background content. In this paper, we propose a binarization algorithm via one-pass local classification. The algorithm first generates the initial binarization results by local thresholding, then corrects the results by a one-pass local classification strategy, followed by the process of component inversion. The experimental results demonstrate that our algorithm achieves a somewhat lower binarization error rate than the state-of-the-art algorithm COS [1], while requiring significantly less computation.