Importance

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Abstract

Digital ink strokes may be fragmented to form a training data set. A neighborhood graph may be formed as a plurality of connected nodes. Relevant features of the training data may be determined in each fragment such as local site features, interaction features, and/or part-label interaction features. Using a conditional random field which may include a hidden random field modeling parameters may be developed to provide a training model to determine a posterior probability of the labels given observed data. In this manner, the training model may be used to predict a label for an observed ink stroke. The modeling parameters may be learned from only a portion of the set of ink strokes in an unsupervised way. For example, many compound objects may include compositional parts. In some cases, appropriate compositional parts may be discovered or inferred during training of the model based on the training data.

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First Claim