Zone-based Keyword Spotting in Bangla and Devanagari Documents
This addresses the problem of efficient keyword retrieval in scanned Indic documents for users in linguistics or digitization, though it is incremental as it builds on prior zone-wise recognition ideas.
The paper tackles word spotting in offline Indic scripts like Bangla and Devanagari by using a zone-based segmentation approach with HMM and combining foreground-background features, resulting in improved performance over traditional methods.
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.