Precise News Video Text Detection/Localization Based on Multiple Frames Integration

This paper presents a multiple frames integration based approach to detect and localize static caption texts on news videos. Utilizing the temporal information of videos, the algorithm includes robust text features and the non-text line deletion technique, and yields precise and tight localization for detected text regions. The Canny edge detector is first applied on reference frames and is followed by executing the logical AND to reduce the edges from the variation of the background including the scrolling texts. Next, rough text candidate regions are determined by calculating the number black-white transition (BWT). Finally, the text regions are refined by the non-text line deletion technique. The proposed algorithm is applicable to multiple languages and robust to text polarities, alignments, and character sizes (from 10×10 to 30×30). According to the experimental results on various multilingual video sequences, the proposed algorithm has a 96% and above performance in recall, precision, and quality of bounding preciseness. Key-Words: information retrieval, multiple frames integration, video text, text detection, Canny edge map, black-white transition