Handwritten word spotting in Indic scripts using foreground and background information

Abstract

In this paper we present a line based word spotting system based on Hidden Markov Model for offline Indic scripts such as Bangla (Bengali) and Devanagari. We propose a novel approach of combining foreground and background information of text line images for keyword-spotting by character filler models. The candidate keywords are searched from a line without segmenting character or words. A significant improvement in performance is noted by using both foreground and background information than anyone alone. Pyramid Histogram of Oriented Gradient (PHOG) feature has been used in our word spotting framework and it outperforms other existing features of word spotting. The framework of combining foreground and background information has been evaluated in IAM dataset (English script) to show the robustness of the proposed approach.