Credit-title detection of video contents based on estimation of superimposed region using character density distribution

We propose a credit-title detection method of video contents based on estimation of superimposed region using character density distribution. Copyright information of video contents is manually extracted for the secondary use of those contents, and its cost is highly expensive. Therefore, automatic detection of credit titles that contain copyright information is highly demanded. However, accuracy of conventional methods is usually insufficient for this purpose. Our method first estimates credit-title-superimposed region based on character density distribution calculated in advance by using many video contents. Then, credit titles are detected in the estimated region. The experiment results show that proposed method improves both recall and precision rates compared to a conventional method. Furthermore, the processing time of the proposed method is less than half that of the conventional method for all contents.