Utilization of texture, contrast and color homogeneity for detecting and recognizing text from video frames

It is possible to index and manage large video archives in a more efficient manner by detecting and recognizing text within video frames. There are some inherent properties of videotext, such as distinguishing texture, higher contrast against background, and uniform color, making it detectable. By employing these properties, it is possible to detect text regions and binarize the image for character recognition. In this paper, a complete framework for detection and recognition of videotext is presented. The results from Gabor-based texture analysis, contrast-based segmentation and color homogeneity are merged to obtain minimum number of candidate regions before binarization. The performance of the system is tested for its recognition rate for various combinations and it is observed that the results give recognition rates, reasonable for most practical purposes.