AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization

Adaptive binarization methods play a central role in document image processing. In this work, an adaptive and parameterless generalization of Otsu's method is presented. The adaptiveness is obtained by combining grid-based modeling and the estimated background map. The parameterless behavior is achieved by automatically estimating the document parameters, such as the average stroke width and the average line height. The proposed method is extended using a multiscale framework, and has been applied on various datasets, including the DIBCO'09 dataset, with promising results.

► Introduction of a parameterless adaptive Otsu binarization method based on the estimated background.
► Introduction of multiscale background estimation.
► Introduction of a skeleton-based postprocessing to remove false positive sub-strokes.
► Improvement of multiscale framework for adaptive binarization methods by removing the bias of the core text pixels.