  Accurately extracting structured data from structure diagrams in financial
announcements is of great practical importance for building financial knowledge
graphs and further improving the efficiency of various financial applications.
First, we proposed a new method for recognizing structure diagrams in financial
announcements, which can better detect and extract different types of
connecting lines, including straight lines, curves, and polylines of different
orientations and angles. Second, we developed a two-stage method to efficiently
generate the industry's first benchmark of structure diagrams from Chinese
financial announcements, where a large number of diagrams were synthesized and
annotated using an automated tool to train a preliminary recognition model with
fairly good performance, and then a high-quality benchmark can be obtained by
automatically annotating the real-world structure diagrams using the
preliminary model and then making few manual corrections. Finally, we
experimentally verified the significant performance advantage of our structure
diagram recognition method over previous methods.
