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.

在本文中，我们为离线印度语脚本（如孟加拉语和孟加拉语）在文本行中提供了一个单词查找系统。最近，已显示出区域明智的识别方法比印度文字中的传统全字识别系统（如Bangla，Devanagari，Gurumukhi）提高了字识别性能（Roy等人，Pattern Recogn 60：1057-1075，26； Bhunia等在模式识别79：12-31，6）。受此想法启发，我们考虑了区域分割方法并使用中间区域信息来改善传统的单词发现性能。为了避免使用启发式方法进行区域分割的问题，我们在此提出一种基于HMM的新方法，用于从文本行图像中分割上下区域分量。从一行中搜索候选关键字，而不分割字符或单词。也，我们提出了一种结合文本行图像的前景和背景信息的特征，以通过字符填充器模型进行关键字发现。通过同时使用前景信息和背景信息而不是单个信息，可以显着提高性能。定向梯度金字塔直方图（PHOG）功能已在我们的单词发现框架中使用。从实验中已经注意到，所提出的基于区域分割的系统优于传统的词点识别方法。定向梯度金字塔直方图（PHOG）功能已在我们的单词发现框架中使用。从实验中已经注意到，所提出的基于区域分割的系统优于传统的词点识别方法。定向梯度金字塔直方图（PHOG）功能已在我们的单词发现框架中使用。从实验中已经注意到，所提出的基于区域分割的系统优于传统的词点识别方法。