We report the findings of a month-long online
competition in which participants developed algorithms for
augmenting the digital version of patent documents published
by the United States Patent and Trademark Office (USPTO).
The goal was to detect figures and part labels in U.S. patent
drawing pages. The challenge drew 232 teams of two, of which 70
teams (30%) submitted solutions. Collectively, teams submitted
1,797 solutions that were compiled on the competition servers.
Participants reported spending an average of 63 hours developing
their solutions, resulting in a total of 5,591 hours of development
time. A manually labeled dataset of 306 patents was used for
training, online system tests, and evaluation. The design and
performance of the top-5 systems are presented, along with a
system developed after the competition which illustrates that
winning teams produced near state-of-the-art results under strict
time and computation constraints. For the 1st place system,
the harmonic mean of recall and precision (f-measure) was
88.57% for figure region detection, 78.81% for figure regions
with correctly recognized figure titles, and 70.98% for part label
detection and character recognition. Data and software from
the competition are available through the online UCI Machine
Learning repository to inspire follow-on work by the image
processing community
