Scene Text Detection and Tracking in Video with Background Cues

To detect scene text in the video is valuable to many content-based video applications. In this paper, we present a novel scene text detection and tracking method for videos, which effectively exploits the cues of the background regions of the text. Specifically, we first extract text candidates and potential background regions of text from the video frame. Then, we exploit the spatial, shape and motional correlations between the text and its background region with a bipartite graph model and the random walk algorithm to refine the text candidates for improved accuracy. We also present an effective tracking framework for text in the video, making use of the temporal correlation of text cues across successive frames, which contributes to enhancing both the precision and the recall of the final text detection result. Experiments on public scene text video datasets demonstrate the state-of-the-art performance of the proposed method.