This paper explores the use of physical signs as anchors for digital 
annotations and other information. In our prototype system, the user carries a 
camera-equipped handheld device with pen-based input to capture street signs, 
restaurant signs, and shop signs. Image matching is supported by interactively 
established point correspondences. The captured image along with context 
information is transferred to a back-end server, which performs image matching 
and returns the results to the user. We present a comparison of four different 
algorithms for the sign matching task. We found that the SIFT algorithm 
performs best. Moreover, we discovered that lighting conditions - especially 
glare - have a crucial impact on the recognition rate.