Text recognition approaches for indoor robotics : a comparison

Lam, Obadiah, Dayoub, Feras, Schulz, Ruth, & Corke, Peter (2014) Text recognition approaches for indoor robotics : a comparison. In 2014 Australasian Conference on Robotics and Automation, 2-4 December 2014, University of Melbourne, Melbourne, VIC.


This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well.

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ID Code: 78741
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 18 Nov 2014 22:58
Last Modified: 04 Dec 2014 20:14

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