The main topic of this thesis is a novel method for the automatic acquisition of mathematical formulas using natural handwriting, speech, and pen gestures. The proposed approach features the integration of all necessary system components in an expectation-driven, single-stage probabilistic decoding procedure which transforms online handwritten and spoken input to a semantic representation of mathematical formulas. An innovation in the field of online formula recognition lies in a statistical assessment of two-dimensional symbol distributions - including also font size variations - within the framework of a context-free grammar. Thus, the structural formula analysis is smoothly embedded into this integral system architecture. As a result, this approach greatly simplifies the problem of character segmentation. Another system feature is the automatic translation of handwritten and typeset formulas into spoken output.
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The main topic of this thesis is a novel method for the automatic acquisition of mathematical formulas using natural handwriting, speech, and pen gestures. The proposed approach features the integration of all necessary system components in an expectation-driven, single-stage probabilistic decoding procedure which transforms online handwritten and spoken input to a semantic representation of mathematical formulas. An innovation in the field of online formula recognition lies in a statistical ass...
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