Support vector machine-based text detection in digital video
Cheol-Woong Shin, K.I. Kim, Myeongseok Park, H.J. Kim · 2002
Textual data within video frames are very useful for describing the contents of the video frames, as they enable both keyword and free-text-based searching. In this paper, we pose the problem of text location in digital video as an example of supervised texture classification and use a support vector machine (SVM) as the texture classifier. Unlike other text detection methods, we do not incorporate any explicit texture feature extraction scheme. Instead, the gray-level values of the raw pixels are directly fed to the classifier. This is based on the observation that a SVM has the capability of learning in a high-dimensional space and of incorporating a feature extraction scheme in its own architecture. In comparison with a neural network-based text detection method, the SVM classifier illustrates the excellence of the proposed method.