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Content Provider | SpringerLink |
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Author | Santosh, K. C. Lamiroy, Bart Wendling, Laurent |
Copyright Year | 2013 |
Abstract | This paper develops a structural symbol recognition method with integrated statistical features. It applies spatial organisation descriptors to the identified shape features within a fixed visual vocabulary that compose a symbol. It builds an attributed relational graph expressing the spatial relations between those visual vocabulary elements. In order to adapt the chosen vocabulary features to multiple and possible specialised contexts, we study the pertinence of unsupervised clustering to capture significant shape variations within a vocabulary class and thus refine the discriminative power of the method. This unsupervised clustering relies on cross-validation between several different cluster indices. The resulting approach is capable of determining part of the pertinent vocabulary and significantly increases recognition results with respect to the state-of-the-art. It is experimentally validated on complex electrical wiring diagram symbols. |
Starting Page | 61 |
Ending Page | 78 |
Page Count | 18 |
File Format | |
ISSN | 14332833 |
Journal | International Journal of Document Analysis and Recognition (IJDAR) |
Volume Number | 17 |
Issue Number | 1 |
e-ISSN | 14332825 |
Language | English |
Publisher | Springer Berlin Heidelberg |
Publisher Date | 2013-06-22 |
Publisher Place | Berlin, Heidelberg |
Access Restriction | One Nation One Subscription (ONOS) |
Subject Keyword | Spatial relations Visual vocabulary Shape descriptor Unsupervised clustering Symbol recognition Image Processing and Computer Vision Pattern Recognition |
Content Type | Text |
Resource Type | Article |
Subject | Computer Science Applications Computer Vision and Pattern Recognition Software |
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