Offline Sketch Parsing via Shapeness Estimation

In this work, we target at the problem of offline
sketch parsing, in which the temporal orders of
strokes are unavailable. It is more challenging
than most of existing work, which usually leverages
the temporal information to reduce the search
space. Different from traditional approaches in
which thousands of candidate groups are selected
for recognition, we propose the idea of shapeness
estimation to greatly reduce this number in a
very fast way. Based on the observation that most
of hand-drawn shapes with well-defined closed
boundaries can be clearly differentiated from nonshapes
if normalized into a very small size, we propose
an efficient shapeness estimation method. A
compact feature representation as well as its efficient
extraction method is also proposed to speed
up this process. Based on the proposed shapeness
estimation, we present a three-stage cascade framework
for offline sketch parsing. The shapeness estimation
technique in this framework greatly reduces
the number of false positives, resulting in a 96.2%
detection rate with only 32 candidate group proposals,
which is two orders of magnitude less than
existing methods. Extensive experiments show the
superiority of the proposed framework over stateof-
the-art works on sketch parsing in both effectiveness
and efficiency, even though they leveraged the
temporal information of strokes.