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We report the findings of a month-long online competition in whichparticipants developed algorithms for augmenting the digital version of patentdocuments 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%) submittedsolutions. Collectively, teams submitted 1,797 solutions that were compiled onthe competition servers. Participants reported spending an average of 63 hoursdeveloping their solutions, resulting in a total of 5,591 hours of developmenttime. A manually labeled dataset of 306 patents was used for training, onlinesystem tests, and evaluation. The design and performance of the top-5 systemsare presented, along with a system developed after the competition whichillustrates that winning teams produced near state-of-the-art results understrict time and computation constraints. For the 1st place system, the harmonicmean of recall and precision (f-measure) was 88.57% for figure regiondetection, 78.81% for figure regions with correctly recognized figure titles,and 70.98% for part label detection and character recognition. Data andsoftware from the competition are available through the online UCI MachineLearning repository to inspire follow-on work by the image processingcommunity.

Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms