Word Spotting Using Radial Descriptor

Word spotting provides an efficient mechanism for word searching and indexing of historical documents. In this paper we present a novel feature descriptor, radial descriptor, and study its application for spotting word parts on Arabic historical documents. The radial descriptor aims to capture the intensity variance of the neighborhood of a point at various scale space levels. Features with high variance along multiple levels are used to describe the shape of a word according to the bag-of-features model. The distance between two word-parts is computed as the distance between their occurrence probability histograms. We have tested our approach on a large dataset of Arabic word-parts and received encouraging results.