Text detection in stores using a repetition prior

Text detection in stores has valuable applications that could transform the shopping experience, yet cluttered store environments present distinct challenges for existing techniques. We propose a strategy for text detection in stores that exploits a repetition prior. Leveraging the fact that shops typically display multiple instances of the same product on the shelf, our approach localizes text regions with a global view of the image, preferring instances that have repeated support in the scene. On two challenging real-world datasets taken with a mobile phone and wearable camera, we demonstrate our method's substantial advantages compared to several state-of-the-art techniques in grocery store environments.