Text analysis using local energy

Abstract

This paper presents a biologically inspired texture-based algorithm using local energy analysis for (1) segmenting text embedded in clutter and (2) classifying text scripts without any explicit knowledge of the type of text present. The local energy model has been shown to work well in texture analysis, where texture segmentation and discrimination are preattentive tasks in human vision. The algorithm for text segmentation involves computing the local energy using a bank of orthogonal pairs of Gabor filters at various orientation and frequency. Similarly, local energy analysis is applied to the task of text script classification using a set of descriptors derived from the local energy information. The segmentation algorithm is quite successful in segmenting text of any arbitrary language script from real images with or without noise. It is also invariant to scale, rotation and position changes in text. The classification scheme was tested on 16 languages with the results obtained consistent with visual classification performed by humans. The scheme is insensitive to orientation of the text script.