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Multi-polarity text segmentation using graph theory

8 years 8 months ago
Multi-polarity text segmentation using graph theory
Text segmentation, or named text binarization, is usually an essential step for text information extraction from images and videos. However, most existing text segmentation methods have difficulties in extracting multi-polarity texts, where multi-polarity texts mean those texts with multiple colors or intensities in the same line. In this paper, we propose a novel algorithm for multipolarity text segmentation based on graph theory. By representing a text image with an undirected weighted graph and partitioning it iteratively, multi-polarity text image can be effectively split into several single-polarity text images. As a result, these text images are then segmented by single-polarity text segmentation algorithms. Experiments on thousands of multi-polarity text images show that our algorithm can effectively segment multi-polarity texts.
Jia Li, YongHong Tian, Tiejun Huang, Wen Gao
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICIP
Authors Jia Li, YongHong Tian, Tiejun Huang, Wen Gao
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