Automatic text extraction in news images using morphology

In this paper we present a new method to extract both superimposed and embedded graphical texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, we convert a color image into a gray-level image and apply contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose + CloseOpen)/2 morphological operations, maintaining text components using (OpenClose + CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, our method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.