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Texts in video provide a rich clue for video indexing and retrieval, yet the detection and recognition of video text remains a challenge. This paper proposes an effective and real-time stroke-based method for text detection in video, which is robust to the change of stroke intensity and width. Particularly, we propose to characterize the text confidence using an edge orientation variance (EOV) and an opposite edge pair (OEP) feature. Based on the text confidence map, candidate text components are extracted and grouped into text lines by thresholding and connected component analysis. Our experimental results demonstrate that the proposed method can detect multilingual texts in video with fairly high accuracy.