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 multi-polarity 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.