Relational data mining and ILP for document image understanding

Abstract

Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on domain-specific knowledge that can be acquired automatically by applying data mining techniques. The spatial dimension of page layout makes classification methods developed in inductive logic programming (ILP) and multi-relational data mining (MRDM) the most suitable candidates for this specific task. In this paper, both approaches are considered and empirically compared on three different data sets consisting of multi-page articles published in an international journal and historical documents. The ILP method is able to learn recursive logical theories that express dependencies between logical components, while the MRDM method extends the nave Bayesian classifier to data stored in multiple tables of a relational database. Experimental results confirm the importance of the spatial dimension for this application and show that the ILP method tends to be conservative with a high (low) percentage of omission (commission) errors, while the probabilistic nature of the MRDM method allows us to tradeoff between the two types of error.