Inductive Logic Programming for Symbol Recognition

Abstract

In this paper, we make an attempt to use inductive logic programming (ILP) to automatically learn non trivial descriptions of symbols, based on a formal description. This work is a first step in this direction and is rather a proof of concept, rather than a fully operational and robust framework. The overall goal of our approach is to express graphic symbols by a number of primitives that may be of any complexity (i.e. not necessarily just lines or points) and connecting relationships that can be deduced from straightforward state-of-the art image treatment and analysis tools. This representation is then used as an input to an ILP solver, in order to deduce non obvious characteristics that may lead to a more semantic related recognition process.