Exploiting Videotext _Events_ for Improved Videotext Detection

Text in video, whether overlay or in-scene, contains a wealth of information vital to automated content analysis systems. However, low resolution of the imagery, coupled with richness of the background and compression artifacts limit the detection accuracy that can be achieved in practice using existing text detection algorithms. This paper presents a novel, non-causal temporal aggregation method that acts as a second pass over the output of an existing text detector over the entire video clip. A multiresolution change detection algorithm is used along the time axis to detect the appearance and disappearance of multiple, concurrent lines of text followed by recursive time-averaged projections on Y and X axes. This algorithm detects and rectifies instances of missed text and enhances spatial boundaries of detected text lines using consensus estimates. Experimental results, which demonstrate significant performance gain on publicly collected and annotated data, are presented.