Model based text detection in images and videos: a learning approach*

Existing methods for text detection in images are simple: most of them are based on texture estimation or edge detection followed by an accumulation of these characteristics. Geometrical constraints are enforced by most of the methods. However, it is done in a morphological post-processing step only. It is obvious, that a weak detection is very difficult — up to impossible — to correct in a post-processing step. We propose a text model which takes into account the geometrical constraints directly in the detection phase: a first coarse detection calculates a text ”probability” image. After wards, for each pixel we calculate geometrical properties of the eventual surrounding text rectangle. These features are added to the features of the first step and fed into a support vector machine classifier.