DMCA
Text Detection in Urban Scenes
| Citations: | 2 - 0 self |
BibTeX
@MISC{Escalera_textdetection,
author = {Sergio Escalera and Xavier Baró and Jordi Vitrià and Petia Radeva},
title = {Text Detection in Urban Scenes},
year = {}
}
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Abstract
Abstract. 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. 1.
Keyphrases
urban scene text detection high variability different text font census transform general methodology visual impaired people multi-linear text region outdoor scene new methodology low false alarm rate tourist assistance cover data set hard task partial occlusion high accuracy spatial information text appearance mobile mapping system classical approach
