MergeCF

-Wolin, Paulson, Hammond

COMMENTS

SUMMARY

MergeCF uses curvature and speed data to find an initial set of corners. It then eliminates false positives by removing similar corners, merging like stroke segments together, and examining stroke segment's direction values.
After producing an initial fit, that algorithm first checks for corners that are together in close proximity and removes the corner with smallest curvature. It then tends to remove the extraneous points that overfit the stroke. It assumes that the corners surrounding the smallest stroke are likely to be false positives. The smallest segment is found, and it is checked if it can be merged with any of its neighboring segments. It is then merged with the neighboring segment that has the least primitive error when combining the two segments.
MergeCF was tested on 501 unistroke symbols. It had an 'all or nothing' accuracy of 66.7 percent which outperforms the Sezgin and Kim algorithms.

DISCUSSION

The algorithm is a big improvement over the current benchmark algorithms. However, I think there might be better ways to remove false positives. I am using a different approach to remove false positives and it seems to work rather accurately. The exact accuracy has not been calculated yet.