On-Line Handwritten Formula Recognition using Hidden Markov Models and Context Dependent Graph Grammars

Andreas Kosmala 1 Gerhard Rigoll 1 Stéphane Lavirotte 2, 3 Loïc Pottier 2, 3
2 LEMME - Software and mathematics
CRISAM - Inria Sophia Antipolis - Méditerranée
3 CAFE - Computer algebra and functional equations
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents an approach for the recognition of on-line handwritten mathematical expressions. The Hidden Markov Model (HMM) based system makes use of simultaneous segmentation and recognition capabilities, avoiding a crucial segmentation during pre-processing. With the segmentation and recognition results, obtained from the HMMrecognizer, it is possible to analyze and interpret the spatial two-dimensional arrangement of the symbols. We use a graph grammar approach for the structure recognition, also used in off-line recognition process, resulting in a general tree-structure of the underlying input-expression. The resulting constructed tree can be translated to any desired syntax (for example: Lisp, LaTeX, OpenMath . . . ).
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Andreas Kosmala, Gerhard Rigoll, Stéphane Lavirotte, Loïc Pottier. On-Line Handwritten Formula Recognition using Hidden Markov Models and Context Dependent Graph Grammars. Fifth International Conference on Document Analysis and Recognition, IEEE Computer Society, Sep 1999, Bangalore, India. ⟨hal-00564645⟩

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