Client-Driven Content Extraction Associated with Table

TL;DR

This paper presents a client-driven method for extracting table content from document images by representing key fields as attributed relational graphs and mining similar graphs to identify table items.

Contribution

It introduces a novel approach using attributed relational graphs to extract table content based on client-identified key fields, validated on real-world industrial data.

Findings

Effective extraction of table content demonstrated on real-world data

Graph-based method accurately identifies table items

Approach tailored to client-specified key fields

Abstract

The goal of the project is to extract content within table in document images based on learnt patterns. Real-world users i.e., clients first provide a set of key fields within the table which they think are important. These are first used to represent the graph where nodes are labelled with semantics including other features and edges are attributed with relations. Attributed relational graph (ARG) is then employed to mine similar graphs from a document image. Each mined graph will represent an item within the table, and hence a set of such graphs will compose a table. We have validated the concept by using a real-world industrial problem.