Adaptive scene-text binarization on images captured by smartphones
Résumé
We address, in this paper, a new adaptive binarization method on images captured by smartphones. This work is part of an application for visually impaired people assistance, that aims at making text information accessible to people who cannot read it. The main advantage of the proposed method is that the windows underlying the local thresh-olding process are automatically adapted to the image content. This avoids the problematic parameter setting of local thresholding approaches, difficult to adapt to a heterogeneous database. The adaptive windows are extracted based on ultimate opening (a morphological operator) and then used as thresholding windows to perform a local Otsu's algorithm. Our method is evaluated and compared with the Niblack, Sauvola, Wolf, TMMS and MSER methods on a new challenging database introduced by us. Our database is acquired by visually impaired people in real conditions. It contains 4000 annotated characters (available online for research purposes). Experiments show that the proposed method outperforms classical binarization methods for degraded images such as low-contrasted or blurred images, very common in our application.
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