Text extraction in digital news video using morphology

Abstract

In this paper, a new method is presented to extract both superimposed and embedded scene texts in digital news videos. The algorithm is summarized in the following three steps: preprocessing, extracting candidate regions, and filtering candidate regions. For the first preprocessing step, a color image is converted into a gray-level image and a modified local adaptive thresholding is applied to the contrast-stretched image. In the second step, various morphological operations and Geo-correction method are applied to remove non-text components while retaining the text components. In the third filtering step, non-text components are removed based on the characteristics of each candidate component such as the number of pixels and the bounding box of each connected component Acceptable results have been obtained using the proposed method on 300 domestic news images with a recognition rate of 93.6%. Also, the proposed method gives good performance on the various kinds of images such as foreign news and film videos.