  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.
