Zone-based keyword spotting in Bangla and Devanagari documents

In this paper, we present a word spotting system in text lines for offline Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown that the zone-wise recognition method improves word recognition performance than the conventional full word recognition system in Indic scripts, like Bangla, Devanagari, Gurumukhi (Roy et al. in Pattern Recogn 60: 1057-1075, 26 ; Bhunia et al. in Pattern Recogn 79: 12–31, 6 ). Inspired from this idea we consider the zone segmentation approach and use middle zone information to improve the traditional word spotting performance. To avoid the problem of zone segmentation using heuristic approach, we propose here a new HMM based approach to segment the upper and lower zone components from the text line images. The candidate keywords are searched from a line without segmenting characters or words. Also, we propose a feature combining foreground and background information of text line images for keyword-spotting by character filler models. A significant improvement in performance is noted by using both foreground and background information instead of the individual one. Pyramid Histogram of Oriented Gradient (PHOG) feature has been used in our word spotting framework. From the experiment, it has been noted that the proposed zone-segmentation based system outperforms traditional approaches of word spotting.