Text Detection Using Delaunay Triangulation in Video Sequence

Text detection and tracking in video sequence is gaining interest due to the challenges posed by low resolution and complex background. This paper proposes a new method for text detection by estimating trajectories between the corners of texts in video sequence over time. Each trajectory is considered as one node to form a graph for all trajectories and Delaunay triangulation is used to obtain edges to connect nodes of the graph. In order to identify the edges that represent text regions, we propose four pruning criteria based on spatial proximity, motion coherence, local appearance and canny rate. This results in several sub-graphs. Then we use depth first search to collect corner points, which essentially represent text candidates. False positives are eliminated using heuristics and missing trajectories will be obtained by tracking the corners in temporal frames. We test the method on different videos and evaluate the method in terms of recall, precision, f-measure with existing results. Experimental result shows that the proposed method is superior to existing method.