  The global Information and Communications Technology (ICT) supply chain is a
complex network consisting of all types of participants. It is often formulated
as a Social Network to discuss the supply chain network's relations,
properties, and development in supply chain management. Information sharing
plays a crucial role in improving the efficiency of the supply chain, and
datasheets are the most common data format to describe e-component commodities
in the ICT supply chain because of human readability. However, with the surging
number of electronic documents, it has been far beyond the capacity of human
readers, and it is also challenging to process tabular data automatically
because of the complex table structures and heterogeneous layouts. Table
Structure Recognition (TSR) aims to represent tables with complex structures in
a machine-interpretable format so that the tabular data can be processed
automatically. In this paper, we formulate TSR as an object detection problem
and propose to generate an intuitive representation of a complex table
structure to enable structuring of the tabular data related to the commodities.
To cope with border-less and small layouts, we propose a cost-sensitive loss
function by considering the detection difficulty of each class. Besides, we
propose a novel anchor generation method using the character of tables that
columns in a table should share an identical height, and rows in a table should
share the same width. We implement our proposed method based on Faster-RCNN and
achieve 94.79% on mean Average Precision (AP), and consistently improve more
than 1.5% AP for different benchmark models.
