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.

翻译：组织架构图是反映组织内部结构、部门组成及岗位间层级关系的重要可视化工具。然而，人工从组织架构图中提取信息往往效率低下且易出错。为解决这一问题，我们提出了一种融合计算机视觉、深度学习与自然语言处理技术的自动化端到端方法。此外，我们设计了一套评价指标，用于衡量所提取信息的完整性与层级准确性。该方法能够清晰、简洁地呈现组织结构，有望改进组织重组流程与资源配置效率。本研究为层级图表分析领域的研究奠定了基础。