Text is one of the richest sources of information in an urban environment. Although textual information is heavily relied on by humans for a majority of the daily tasks, its usage has not been completely exploited in the field of robotics. In this work, we propose a localization approach utilizing textual features in urban environments. Starting at an unknown location, equipped with an RGB-camera and a compass, our approach uses off-the-shelf text extraction methods to identify text labels in the vicinity. We then apply a probabilistic localization approach with specific sensor models to integrate multiple observations. An extensive evaluation with real-world data gathered in different cities reveals an improvement over GPS-based localization when using our method.