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READING STREET SIGNS USING A GENERIC STRUCTURED OBJECT DETECTION AND SIGNATURE RECOGNITION APPROACH
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2009 (English)In: VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 2, SETUBAL: INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION , 2009, 346-355 p.Conference paper, Published paper (Refereed)
Abstract [en]

In the paper we address the applied problem of detecting and recognizing street name plates in urban images by a generic approach to structural object detection and recognition. A structured object is detected using a boosting approach and false positives are filtered using a specific method called the texture transform. In a second step the subregion containing the key information, here the text, is segmented out. Text is in this case characterized as texture and a texton based technique is applied. Finally the texts are recognized by using Dynamic Time Warping on signatures created from the identified regions. The recognition method is general and only requires text in some form, e.g. a list of printed words, but no image models of the plates for learning. Therefore, it can be shown to scale to rather large data sets. Moreover, due to its generality it applies to other cases, such as logo and sign recognition. On the other hand the critical part of the method lies in the detection step. Here it relied on knowledge about the appearance of street signs. However, the boosting approach also applies to other cases as long as the target region is structured in some way. The particular scenario considered deals with urban navigation and map indexing by mobile users, e.g. when the images are acquired by a mobile phone.

Place, publisher, year, edition, pages
SETUBAL: INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION , 2009. 346-355 p.
Keyword [en]
Structural object detection, Text detection, Text segmentation, Text recognition
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-30762ISI: 000268137500056Scopus ID: 2-s2.0-70349286123ISBN: 978-989-8111-69-2 (print)OAI: oai:DiVA.org:kth-30762DiVA: diva2:401801
Conference
4th International Conference on Computer Vision Theory and Applications, Lisbon, PORTUGAL, FEB 05-08, 2009
Note
QC 20110304Available from: 2011-03-04 Created: 2011-03-04 Last updated: 2011-03-04Bibliographically approved

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Naderi Parizi, SobhanTavakoli Targhi, AlirezaAghazadeh, OmidEklundh, Jan-Olof
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