Text recognition approaches for indoor robotics : a comparison
Data(s) |
02/12/2014
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Resumo |
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. |
Formato |
application/pdf application/zip application/zip |
Identificador | |
Relação |
http://eprints.qut.edu.au/78741/1/final_version_pap138s1-file1.pdf http://eprints.qut.edu.au/78741/7/roomlabeldataset.zip http://eprints.qut.edu.au/78741/8/roomlabeldataset_crop.zip 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. http://purl.org/au-research/grants/ARC/DP140103216 |
Direitos |
Copyright 2014 [please consult the author] |
Fonte |
ARC Centre of Excellence for Robotic Vision; School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080000 INFORMATION AND COMPUTING SCIENCES |
Tipo |
Conference Paper |