利用局部描述符的空间组织识别词和符号

Marçal Rusiñol, J. Lladós
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引用次数: 35

摘要

在本文中,我们提出了一种在接线图图像集合中识别文本和图形符号的方法。单词识别和符号识别方法倾向于使用最具区别性的特征来描述待定位的对象。这一事实使得人们不能同时处理文本信息和符号信息。我们提出了一种能够索引单词和符号的定位架构,灵感来自现成的对象识别架构。从文档图像中提取关键点,并在每个感兴趣的点处计算一个局部描述符。这些描述符的空间组织验证了在特定位置和特定姿势下找到对象(文本或符号)的假设。
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Word and Symbol Spotting Using Spatial Organization of Local Descriptors
In this paper we present a method to spot both text and graphical symbols in a collection of images of wiring diagrams. Word spotting and symbol spotting methods tend to use the most discriminative features to describe the objects to be located. This fact makes that one can not tackle with textual and symbolic information at the same time. We propose a spotting architecture able to index both words and symbols, inspired in off-the-shelf object recognition architectures. Keypoints are extracted from a document image and a local descriptor is computed at each of these points of interest. The spatial organization of these descriptors validate the hypothesis to find an object (text or symbol) in a certain location and under a certain pose.
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