Apply SOM to Video Artificial Text Area Detection

Abstract

Video artificial text detection is a challenging problem of pattern recognition. Current methods which are usually based on edge, texture, connected domain, feature or learning are always limited by size, location, language of artificial text in video. To solve the problems mentioned above, this paper applied SOM (Self-Organizing Map) based on supervised learning to video artificial text detection. First, text features were extracted. And considering the video artificial text's limitations mentioned, artificial text’s location and gradient of each pixel were used as the features which were used to classify. Then three layers supervised SOM was proposed to classify the text and non-text areas in video image. At last, the morphologic operating was used to get a much more accurate result of text area. Experiments showed that this method could locate and detect artificial text area in video efficiently.