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Integrating vocabulary clustering with spatial relations for symbol recognition

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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.

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Authors and Affiliations

  1. LORIA (UMR 7503), Université de Lorraine, Campus Scientifique, BP 239, 54506 , Vandoeuvre-lés-Nancy Cedex, France

    K. C. Santosh & Bart Lamiroy

  2. SIP, LIPADE, Université Paris Descartes (Paris V), 45, rue des Saints-Pères, 75270 , Paris Cedex 06, France

    Laurent Wendling

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  1. K. C. Santosh
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  2. Bart Lamiroy
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  3. Laurent Wendling
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Correspondence to K. C. Santosh.

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Santosh, K.C., Lamiroy, B. & Wendling, L. Integrating vocabulary clustering with spatial relations for symbol recognition. IJDAR 17, 61–78 (2014). https://doi.org/10.1007/s10032-013-0205-4

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