A New Wavelet-Median-Moment based Method for Mult- Oriented Video Text Detection

In this paper, we present a new method based on wavelet-medianmoments and a novel idea of angle projection for detecting multioriented text in video. The proposed method uses wavelet decomposition first to obtain three high frequency sub-bands (LH, HL and HH) and then median central moments are computed on the average sub-bands of the three high frequency sub-bands to brighten the text pixels. K-means clustering (K=2) is used for obtaining text pixels from the wavelet-median-moments features (WMMF). Text candidates are obtained by mapping the output of K-means on Sobel edge map of the original input frame. To deal with multi-oriented text, we introduce new idea of Angle Projection (AP) based on boundary growing and nearest neighbor concepts from the text candidates instead of conventional projection profiles. The proposed method is experimented on horizontal text data, non-horizontal text data, temporal data, nontext data and camera based images (ICDAR-03 scene text data) to show that the proposed method is superior to existing methods in terms of metrics.