  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 zone-wise recognition method improves the word recognition performance
than conventional full word recognition system in Indic scripts. Inspired with
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 an 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 novel 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 than their 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.
