A robust model for salient text detection in natural scene images using MSER feature detector and Grabcut

Visual attention models have been used to recognize the most prominent region in a natural scene. These regions are going to pull the human attention. The state-of-art models keep on under-predicting the significant image regions having text. These are specifically the regions with most noteworthy semantic significance in a natural scene and turn out to be useful for saliency-based applications like image classification and captioning. The text or character detection as a salient region in image remains a challenging research problem. Text contents within the scene convey vital information about the image. For example, signboard content conveys the important information for visually impaired person. In this paper, we have proposed a new model for salient text detection in a natural scene. In the proposed model, we integrate saliency model with the segmentation and text detection approach in a natural scene to generate the text saliency. The experimental outcomes in ROC curve and DET curves illustrate that the proposed model outperformed the state-of-art methods for detection of salient text content from a natural scene.