015 - OItmans' PhD Thesis

Summary:

Oltmans takes the approach that sketch data within features has too much noise and shouldn't be relied upon as much. This paper supports the idea that visualization techniques should be used instead of data to get more "humanly" recognition. The system described uses a "bullseye" technique that places a circle in essence every 5 pixels, with smaller circles cocentric to the larger circle in dartboard fashion. The bullseye is rotated freely, and includes stroke direction of all points within the bullseye, in "bins". The bins are made into frequency vectors, which are then compared to a pre-defined codebook The system then goes along the entire stroke with set windows and measure the ink of the line with all the known data and clusters all shapes that are close together. It then splits the clusters into more clusters to make individual shapes in a trickle-down fashion. Evaluation of the system was around 90% accurate in most areas.

Discussion:

I think this is an interesting outlook. While I don't believe throwing away all the data we have from the strokes is necessary, I do like the fact that you can get such high recognition without all the corner-finding algorithms and such that so many use. I'm a strong believer in editable thresholds, though, and I think that some things might be subject to change, such as the 5 pixel radius of the circles. Other than that, the idea is definitely something to consider for future work.