Video OCR: Indexing Digital News Libraries by Recognition of Superimposed Caption

Abstract

The automatic extraction and reading of news captions and annotations can be of great help locating topics of interest in digital news video archives. To achieve this goal, we present a technique, called Video OCR, which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low resolution characters and extremely complex backgrounds, we apply an interpolation filter, multi-frame integration and a combination of four filters. Segmenting characters is done by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using the text-like properties and the use of a language-based post-processing technique to increase word recognition rates. The overall recognition results are satisfactory 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.