Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Automatic Caption Localization in Compressed Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Robust Real-Time Face Detection
International Journal of Computer Vision
An automatic sign recognition and translation system
Proceedings of the 2001 workshop on Perceptive user interfaces
Text Detection from Natural Scene Images: Towards a System for Visually Impaired Persons
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Text detection in urban scenes is a hard task due to the high variability of text appearance: different text fonts, changes in the point of view, or partial occlusion are just a few problems. Text detection can be specially suited for georeferencing business, navigation, tourist assistance, or to help visual impaired people. In this paper, we propose a general methodology to deal with the problem of text detection in outdoor scenes. The method is based on learning spatial information of gradient based features and Census Transform images using a cascade of classifiers. The method is applied in the context of Mobile Mapping systems, where a mobile vehicle captures urban image sequences. Moreover, a cover data set is presented and tested with the new methodology. The results show high accuracy when detecting multi-linear text regions with high variability of appearance, at same time that it preserves a low false alarm rate compared to classical approaches.