  Organizational charts, also known as org charts, are critical representations
of an organization's structure and the hierarchical relationships between its
components and positions. However, manually extracting information from org
charts can be error-prone and time-consuming. To solve this, we present an
automated and end-to-end approach that uses computer vision, deep learning, and
natural language processing techniques. Additionally, we propose a metric to
evaluate the completeness and hierarchical accuracy of the extracted
information. This approach has the potential to improve organizational
restructuring and resource utilization by providing a clear and concise
representation of the organizational structure. Our study lays a foundation for
further research on the topic of hierarchical chart analysis.

Computer Vision and Pattern Recognition;Artificial Intelligence