A robust statistic method for classifying color polarity of video text

Video text extraction and recognition are prerequisite tasks for video indexing and retrieval. Color polarity classification of video text is very important to these tasks. Most existing text extraction methods assume that the text color is always light (or dark). Obviously, this assumption restricts the application of these methods to some specific domains. Only a few methods can detect the color polarity on condition that the background is clear. However, many real video texts have various appearances and complex backgrounds that existing methods cannot handle. This paper proposes a statistic color polarity classification method that is robust to various background complexities, font styles, stroke widths, and languages. We discover the intrinsic relationships between text edges and background edges, and then develop an efficient measurement to detect the color polarity. The experimental results show that the proposed method achieves a much higher accuracy, 98.5%, than existing methods.