  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.
