Graphics and Scene Text Classification in Video

Achieving good accuracy for text detection and recognition is a challenging and interesting problem in the field of video document analysis because of the presences of both graphics text that has good clarity and scene text that is unpredictable in video frames. Therefore, in this paper, we present a novel method for classifying graphics texts and scene texts by exploiting temporal information and finding the relationship between them in video. The method proposes an iterative procedure to identify Probable Graphics Text Candidates (PGTC) and Probable Scene Text Candidates (PSTC) in video based on the fact that graphics texts in general do not have large movements especially compared to scene texts which are usually embedded on background. In addition to PGTC and PSTC, the iterative process automatically identifies the number of video frames with the help of a converging criterion. The method further explores the symmetry between intra and inter character components to identify graphics text candidates and scene text candidates. Boundary growing method is employed to restore the complete text line. For each segmented text line, we finally introduce Eigen value analysis to classify graphics and scene text lines based on the distribution of respective Eigen values. Experimental results with the existing methods show that the proposed method is effective and useful to improve the accuracy of text detection and recognition.