We describe SnooperText, an original detector for textual information embedded in photos of building façades (such as names of stores, products and services) that we developed for the iTowns urban geographic information project. SnooperText locates candidate characters by using toggle-mapping image segmentation and character/non-character classification based on shape descriptors. The candidate characters are then grouped to form either candidate words or candidate text lines. These candidate regions are then validated by a text/non-text classifier using a HOG-based descriptor specifically tuned to single-line text regions. These operations are applied at multiple image scales in order to suppress irrelevant detail in character shapes and to avoid the use of overly large kernels in the segmentation. We show that SnooperText outperforms other published state-of-the-art text detection algorithms on standard image benchmarks. We also describe two metrics to evaluate the end-to-end performance of text extraction systems, and show that the use of SnooperText as a pre-filter significantly improves the performance of a general-purpose OCR algorithm when applied to photos of urban scenes.