Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition

TL;DR

This paper introduces a novel approach to automatically recognize complex table structures in electronic datasheets within ICT supply chains, enhancing data processing efficiency through a specialized object detection method.

Contribution

It formulates Table Structure Recognition as an object detection problem and proposes a cost-sensitive loss and anchor generation method tailored for complex table layouts.

Findings

Achieved 94.79% mean Average Precision (AP) on benchmark datasets.

Improved AP by more than 1.5% over existing models.

Enhanced automatic processing of tabular data in supply chain datasheets.

Abstract

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.