A Statistical and Structural Approach for Symbol Recognition, Using XML Modelling

Abstract : This paper deals with the problem of symbol recognition in technical document interpretation. We present a system using a statistical and structural approach. This system uses two interpretation levels. In a first level, the system extracts and recognizes the loops of symbols. In the second level, it relies on proximity relations between the loops in order to rebuild loop graphs, and then to recognize the complete symbols. Our aim is to build a generic device, so we have tried to outsource models descriptions and tools parameters. Data manipulated by our system are modelling in XML. This gives the system the ability to interface tools using different communication data structures, and to create graphic representation of process results.
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Terry Caelli, Adnan Amin, Robert P. W. Duin, Dick de Ridder and Mohamed Kamel. International Joint Workshops on Statistical Pattern Recognition and Syntactic and Structural Pattern Recognition, 2002, France. Springer, 2396, pp.281-290, 2002, Lecture Notes in Computer Science. 〈10.1007/3-540-70659-3_29〉
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Mathieu Delalandre, Pierre Héroux, Sébastien Adam, Eric Trupin, Jean-Marc Ogier. A Statistical and Structural Approach for Symbol Recognition, Using XML Modelling. Terry Caelli, Adnan Amin, Robert P. W. Duin, Dick de Ridder and Mohamed Kamel. International Joint Workshops on Statistical Pattern Recognition and Syntactic and Structural Pattern Recognition, 2002, France. Springer, 2396, pp.281-290, 2002, Lecture Notes in Computer Science. 〈10.1007/3-540-70659-3_29〉. 〈hal-00605889〉

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