Local variance image-based for scene text binarization under illumination effects

Abstract

Illumination effects, especially shadow and lighting condition, are grand challenges for scene-text localization. With these effects, text localization faces a difficult task to discriminate text regions from a nature scene due to edge and detail of characters affected by surrounding environments. To improve effectiveness of the scene-text localization, this paper proposes a local variance image technique to enhance character's edge for easily segmenting the scene text from the back-ground under illumination effects. In this method, the local variance image plays an important role in indicating how high complexity is in each local area. Then the proposed adaptive kernel size thresholding method is applied to such a local variance image to segment the scene text from the background. When the proposed method is tested with a Thai text dataset, the experimental results show the scene-text binarization is better than those of the state-of-the-art methods.