Scene text detection using sparse stroke information and MLP

In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling of the distance transform values is a novel approach. Another feature is the distance between the foreground and background colors computed in a perceptually uniform and illumination-invariant color space. Remaining features include two ratios of anti-parallel edge gradient orientations, a regularity measure between the skeletal representation and Canny edgemap of the object, average edge gradient magnitude, variation in the foreground gray levels and five others. Here, we present the results of the proposed approach on the ICDAR 2003 database and another database of scene images consisting of text of Indian scripts.