Text recognition on traffic panels from street-level imagery

Text detection and recognition in images taken in uncontrolled environments still remains a challenge in computer vision. This paper presents a method to extract the text depicted in road panels in street view images as an application to Intelligent Transportation Systems (ITS). It applies a text detection algorithm to the whole image together with a panel detection method to strengthen the detection of text in road panels. Word recognition is based on Hidden Markov Models, and a Web Map Service is used to increase the effectiveness of the recognition. In order to compute the distance from the vehicle to the panels, a function that estimates the distance in meters from the text height in pixels has been obtained. After computing the direction vector of the vehicle, world coordinates are computed for each panel. Experimental results on real images from Google Street View prove the efficiency of our proposal and give way to using street-level images for different applications on ITS such as traffic signs inventory or driver assistance.