DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing
  Understanding
Kehinde Ajayi Xin Wei Martin Gryder Winston Shields Jian Wu Shawn M. Jones Michal Kucer Diane Oyen
Recent advances in computer vision (CV) and natural language processing have
been driven by exploiting big data on practical applications. However, these
research fields are still limited by the sheer volume, versatility, and
diversity of the available datasets. CV tasks, such as image captioning, which
has primarily been carried out on natural images, still struggle to produce
accurate and meaningful captions on sketched images often included in
scientific and technical documents. The advancement of other tasks such as 3D
reconstruction from 2D images requires larger datasets with multiple
viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more
than 2.7 million technical drawings with 132,890 object names and 22,394
viewpoints extracted from 14 years of US design patent documents. We
demonstrate the usefulness of DeepPatent2 with conceptual captioning. We
further provide the potential usefulness of our dataset to facilitate other
research areas such as 3D image reconstruction and image retrieval.