Structure Diagram Recognition in Financial Announcements
This work addresses the practical need for accurate data extraction from financial diagrams to build knowledge graphs and improve financial application efficiency, representing a domain-specific advancement.
The paper tackles the problem of extracting structured data from structure diagrams in financial announcements by proposing a new recognition method that better detects various connecting lines and developing a two-stage approach to create the industry's first benchmark from Chinese financial announcements, with experimental verification showing significant performance advantages over previous methods.
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.