Novel Edge Features for Text Frame Classification in Video

Abstract

Text frame classification is needed in many applications such as event identification, exact event boundary identification, navigation, video surveillance in multimedia etc. To the best of our knowledge, there are no methods reported solely dedicated to text frame classifications so far. Hence this paper presents a new approach to text frame classification in video based on capturing local observable edge properties of text frames, by virtue of the strong presence of sharp edges, straight appearances of edges and consistent proximity between edges. The approach initially classifies the blocks of the frame into text blocks and non-text blocks. The true text block is then identified among classified text blocks to detect text frames by the proposed features. If the text frame produces one true text block then it is considered as a text frame otherwise a non-text frame. We evaluate the proposed approach on a large database containing both text and nontext frames and publicly available data at two levels, i.e., estimating recall and precision at the block level and the frame level.