Video OCR for digital news archive

Abstract

Video OCR is a technique that can greatly help to locate topics of interest in a large digital news video archive via the automatic extraction and reading of captions and annotations. News captions generally provide vital search information about the video being presented, the names of people and places or descriptions of objects. In this paper, two difficult problems of character recognition for videos are addressed: low resolution characters and extremely complex backgrounds. We apply an interpolation filter, multi-frame integration and a combination of four filters to solve these problems. Segmenting characters is done by a recognition-based segmentation method and intermediate character recognition results are used to improve the segmentation. The overall recognition results are good enough for use in news indexing. Performing video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.