NDLI: Using text-spotting to query the world

Content Provider	IEEE Xplore Digital Library
Author	Posner, I. Corke, P. Newman, P.
Copyright Year	2010
Description	Author affiliation: Mobile Robotics Group, Department of Engineering Science, Oxford University, UK (Posner, I.; Newman, P.) \|\| School of Engineering Systems, Queensland University of Technology, Australia (Corke, P.)
Abstract	The world we live in is labeled extensively for the benefit of humans. Yet, to date, robots have made little use of human readable text as a resource. In this paper we aim to draw attention to text as a readily available source of semantic information in robotics by implementing a system which allows robots to read visible text in natural scene images and to use this knowledge to interpret the content of a given scene. The reliable detection and parsing of text in natural scene images is an active area of research and remains a non-trivial problem. We extend a commonly adopted approach based on boosting for the detection and optical character recognition (OCR) for the parsing of text by a probabilistic error correction scheme incorporating a sensor-model for our pipeline. In order to interpret the scene content we introduce a generative model which explains spotted text in terms of arbitrary search terms. This allows the robot to estimate the relevance of a given scene with respect to arbitrary queries such as, for example, whether it is looking at a bank or a restaurant. We present results from images recorded by a robot in a busy cityscape.
Starting Page	3181
Ending Page	3186
File Size	2466418
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5653151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Optical character recognition software Training Pixel Mathematical model Probabilistic logic Pipelines
Content Type	Text
Resource Type	Article

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