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In this paper, we propose a novel method for text detection in natural scenes. Gradient Vector Flow is first used to extract both intra-character and inter-character symmetries. In the second step, we group horizontally aligned symmetry components into text lines based on several constraints on sizes, positions and colors. Finally, to remove false positives, we employ a learning-based approach which makes use of Histogram of Oriented Gradients feature. The main advantage of the proposed method lies in the use of both the text features and the gap (i.e., inter-character) features. Existing techniques typically extract only the former and ignore the latter. Experiments on the benchmark ICDAR 2003 dataset show the good detection performance of our method on natural scene text.