Optical flow based dynamic curved video text detection

Text detection in video is a challenging problem as it is useful in several real time applications in the field of video indexing and retrieval. Unlike existing methods that generally focus on horizontal caption or graphics text, the proposed method focuses on detecting dynamic curved text in video. The method explores the characteristics of the optical flow of text, namely, constant velocity, uniform magnitude distribution and unique angle distribution, to identify text candidates with the help of k-means clustering algorithm. We propose an iterative procedure which finds the standard deviation of text candidates between the first and its successive frames, and it terminates when there is a sudden decrease in the standard deviation values. The proposed method eliminates false text candidates based on the characteristics of optical flow at component level while retaining the potential text candidates. Then, direction guided boundary growing is proposed to traverse curved text lines in video. Furthermore, the characteristics of optical flow of text are utilized at block level to eliminate false positives. Experiments are conducted with various videos, including video with static text, static and dynamic text, and dynamic text only, to evaluate the proposed method. The results are benchmarked with the existing methods to verify the superiority of our method over the existing methods in terms of recall, precision, F-measure and average processing time.