SnooperText: A Text Detection System for Automatic Indexing of Urban Scenes

Computer Vision and Image Understanding (CVIU), 2014.

Rodrigo Minetto ,   Nicolas Thome ,   Matthieu Cord ,   Neucimar J. Leite ,   Jorge Stolfi

Federal University of Technology of Parana, DAINF, Curitiba, Brazil
Universite Pierre et Marie Curie, UPMC-Sorbonne Universities, LIP6, Paris, France
University of Campinas, Institute of Computing, Campinas, Brazil

SnooperText is an original detector for textual information embedded in photos of building facades (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. SnooperText outperforms other published state-of-the-art text detection algorithms on standard image benchmarks, as described in our journals [text post-filtering] [text detection].

Source code (Java) and the executable:

[click here to download]