A simple and fast text localization algorithm for indoor mobile robot navigation

Abstract

The automatic detection of Region of Interests (ROI) is an active research area in the design of machine vision systems. By using bottom-up image processing algorithms to predict human eye fixations or focus of attention and extract the relevant embedded information content in images has been widely applied in this area, especially in mobile robot navigation. Text that appears in images contains large quantities of useful information. Further more, many potential landmarks in mobile robot navigation contain text, such as nameplates, information signs and hence scene text is an important feature to be extracted. In this paper, we propose a simple and fast text localization algorithm based on a zero-crossing operator, which can effectively detect text-based features in an indoor environment for mobile robot navigation. This method is based on the idea that high local spatial variance is one of the distinguishing characteristics of text. Text in images has distinct intensity/color differences relative to its neighbourhood background and appears in clusters with uniform inter-character distance. If we compute the spatial variance along the text line we can get a large value, while the spatial variance in the background is fairly low. Experimental results show that calculating the spatial variance to detect text-based landmarks in real-time is an effective and efficient method for mobile robot navigation.