Unsupervised Segmentation of Text Fragments in Real Scenes

This paper proposes a method that aims to reduce a real scene to a set of regions that contain text fragments and keep small number of false positives. Text is modeled and characterized as a texture pattern, by employing the QMF wavelet decomposition as a texture feature extractor. Processing includes segmentation and spatial selection of regions and then content-based selection of fragments. Unlike many previous works, text fragments in different scales and resolutions laid against complex backgrounds are segmented without supervision. Tested in four image databases, the method is able to reduce visual noise to 4.69% and reaches 96.5% of coherency between the localized fragments and those generated by manual segmentation.