1. A method of distinguishing between text strokes and non-text strokes in digital ink, the method comprising: detecting a stroke in the digital ink; generating a classification model based on at least one stroke feature of the stroke, wherein the model defines a posterior probability that the stroke is a text stroke; labeling the stroke as a text stroke or a non-text stroke based on the posterior probability; and detecting the at least one stroke feature of the stroke as a length-width ratio of a total least squares model fitted to the stroke in the digital ink.
1. A method of distinguishing between text strokes and non-text strokes in digital ink, the method comprising: detecting a stroke in the digital ink; generating a classification model based on at least one stroke feature of the stroke, wherein the model defines a posterior probability that the stroke is a text stroke; labeling the stroke as a text stroke or a non-text stroke based on the posterior probability; and detecting the at least one stroke feature of the stroke as a length-width ratio of a total least squares model fitted to the stroke in the digital ink. 8. The method of claim 1 further comprising: dividing the stroke into fragments at points corresponding to local maxima in the curvature of the stroke; and detecting the at least one stroke feature of the stroke as a direction of the largest fragment of the stroke in the digital ink.