On the Scalability of Graphic Symbol Recognition

This paper deals with a complex symbol recognition process considering a large number of classes and only one training image per class. Furthermore, the response times of recognition system should be short and the interpretation of results must be easy. In this particular case, both statistical and structural methods are not the most suitable. A new composite descriptor and a similarity measure are proposed. Experimental results show the proposed method outperforms two descriptors widely used in symbol recognition with industrial data.

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