2D and 3D Video Scene Text Classification

Text detection and recognition is a challenging problem in document analysis due to the presence of the unpredictable nature of video texts, such as the variations of orientation, font and size, illumination effects, and even different 2D/3D text shadows. In this paper, we propose a novel horizontal and vertical symmetry feature by calculating the gradient direction and the gradient magnitude of each text candidate, which results in Potential Text Candidates (PTCs) after applying the k-means clustering algorithm on the gradient image of each input frame. To verify PTCs, we explore temporal information of video by proposing an iterative process that continuously verifies the PTCs of the first frame and the successive frames, until the process meets the converging criterion. This outputs Stable Potential Text Candidates (SPTCs). For each SPTC, the method obtains text representatives with the help of the edge image of the input frame. Then for each text representative, we divide it into four quadrants and check a new Mutual Nearest Neighbor Symmetry (MNNS) based on the dominant stroke width distances of the four quadrants. A voting method is finally proposed to classify each text block as either 2D or 3D by counting the text representatives that satisfy MNNS. Experimental results on classifying 2D and 3D text images are promising, and the results are further validated by text detection and recognition before classification and after classification with the exiting methods, respectively.