TV viewers want to grasp the contents of the news program in a short time due to the increasing number of news channels. Conventional summarization methods based on extraction of the important sentences from each topic included in the news speech is insufficient because the important sentences can not always be extracted from each topic due to unknown topic boundary. To solve this problem, in this paper, we propose a summarization method of TV news program by segmenting the news speech into topics and then extracting the important sentence from each topic.

The purpose of this study is to automatically extract telop and flip characters. The process starts with the extraction of stable frame sections including the telop and flip characters. The next process is character region extraction and we propose the application of local line density to discriminate the telop characters from other elements such as lines and symbols. The final process is telop character extraction and we show the effectiveness of floating adaptive three level thresholding (FATLT) which thresholds the image intensity into three levels and finally binarizes the character regions precisely even at low contrast, taking topological relationships between characters and their background into consideration.