Extracting Network Structures from Corporate Organization Charts Using
  Heuristic Image Processing
Hiroki Sayama Junichi Yamanoi
Organizational structure of corporations has potential to provide
implications for dynamics and performance of corporate operations. However,
this subject has remained unexplored because of the lack of readily available
organization network datasets. To overcome the this gap, we developed a new
heuristic image-processing method to extract and reconstruct organization
network data from published organization charts. Our method analyzes a PDF file
of a corporate organization chart and detects text labels, boxes, connecting
lines, and other objects through multiple steps of heuristically implemented
image processing. The detected components are reorganized together into a
Python's NetworkX Graph object for visualization, validation and further
network analysis. We applied the developed method to the organization charts of
all the listed firms in Japan shown in the ``Organization Chart/System Diagram
Handbook'' published by Diamond, Inc., from 2008 to 2011. Out of the 10,008
organization chart PDF files, our method was able to reconstruct 4,606
organization networks (data acquisition success rate: 46%). For each
reconstructed organization network, we measured several network diagnostics,
which will be used for further statistical analysis to investigate their
potential correlations with corporate behavior and performance.