Summary
This system is supposed to distinguish between text and graphics. What they do is to first extract a set of features out of strokes, such as it direction, length width ratio of it, total curvature, etc. Subsequently, it takes uses features of the gaps between strokes and finally by incorporating an HMM, they put all these in context of a sequence.
Their experiments demonstrated the usefulness of temporal context however the advantage of incorporating gap information is not evident in their results
Discussion
As they mention in the paper, they have ignored the length of the gaps, so it would be better to regard stroke within which there is a large gap, independent. However using a temporal model, in this case HMM for the text and graphics seemed reasonable and worked well too
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