  Diagram parsing is an important foundation for geometry problem solving,
attracting increasing attention in the field of intelligent education and
document image understanding. Due to the complex layout and between-primitive
relationship, plane geometry diagram parsing (PGDP) is still a challenging task
deserving further research and exploration. An appropriate dataset is critical
for the research of PGDP. Although some datasets with rough annotations have
been proposed to solve geometric problems, they are either small in scale or
not publicly available. The rough annotations also make them not very useful.
Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a
novel annotation method. Our dataset consists of 5000 diagram samples composed
of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text
types. Different from previous datasets, our PGDP5K dataset is labeled with
more fine-grained annotations at primitive level, including primitive classes,
locations and relationships. What is more, combined with above annotations and
geometric prior knowledge, it can generate intelligible geometric propositions
automatically and uniquely. We performed experiments on PGDP5K and
IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves
only 66.07% F1 value. This shows that PGDP5K presents a challenge for future
research. Our dataset is available at
http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.
