Detection of Text on Road Signs From Video

Wen Wu, Member, IEEE, Xilin Chen, Member, IEEE, and Jie Yang, Member, IEEE

Abstract
A fast and robust framework for incrementally detecting
text on road signs from video is presented in this paper.
This new framework makes two main contributions. 1) The
framework applies a divide-and-conquer strategy to decompose
the original task into two subtasks, that is, the localization of road
signs and the detection of text on the signs. The algorithms for the
two subtasks are naturally incorporated into a unified framework
through a feature-based tracking algorithm. 2) The framework
provides a novel way to detect text from video by integrating
two-dimensional (2-D) image features in each video frame (e.g.,
color, edges, texture) with the three-dimensional (3-D) geometric
structure information of objects extracted from video sequence
(such as the vertical plane property of road signs). The feasibility
of the proposed framework has been evaluated using 22 video
sequences captured from a moving vehicle. This new framework
gives an overall text detection rate of 88.9% and a false hit rate of
9.2%. It can easily be applied to other tasks of text detection from
video and potentially be embedded in a driver assistance system.

Index Terms
Object detection from video, road sign detection,
text detection, vehicle navigation.

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