Abstract
Decision trees have been widely used for different tasks in artificial intelligence and data mining. Tree automata have been used in pattern recognition tasks to represent some features of objects to be classified. Here we propose a method that combines both approaches to solve a classical problem in pattern recognition such as Optical Character Recognition. We propose a method which is organized in two stages: (1) we use a grammatical inference technique to represent some structural features of the characters and, (2) we obtain edit distances between characters in order to design a decision tree. The combination of both methods benefits from their individual characteristics and is formulated as a coherent unifying strategy.
Work supported by the Spanish CICYT under contract TIC2000-1153.
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Sempere, J.M., López, D. (2003). Learning Decision Trees and Tree Automata for a~Syntactic Pattern Recognition Task. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_109
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DOI: https://doi.org/10.1007/978-3-540-44871-6_109
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