Statistical Visual Language Models for Ink Parsing
نویسندگان
چکیده
In this paper we motivate a new technique for automatic recognition of hand-sketched igital ink. By viewing sketched drawings as utterances in a visual language, sketch recognition can be posed as an ambiguous parsing problem. On this premise we have developed an algorithm for ink parsing that uses a statistical model to disambignate. Under this formulation, writing a new recognizer for a visual language is as simple as writing a declarative grammar for the language, generating a model from the grammar, and ffaining the model on drawing examples. We evaluate the speed and accuracy of this approach for the sample domain of the SILK visual language and r~)ort positive initial results.
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