Inductive Logic Programming for Symbol Recognition

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. Th...

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Main Authors: Santosh, K.C., Lamiroy, B., Ropers, J.-P.
Format: Conference Proceeding
Language:English
Subjects:
classification
Data mining
Electronic mail
Graphics
Image analysis
inductive logic programming
Joining processes
Logic programming
Robustness
symbol recognition
Text analysis
Vocabulary
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Lamiroy, B.
Ropers, J.-P.
description 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.
doi_str_mv 10.1109/ICDAR.2009.166
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects classification
Data mining
Electronic mail
Graphics
Image analysis
inductive logic programming
Joining processes
Logic programming
Robustness
symbol recognition
Text analysis
Vocabulary
title Inductive Logic Programming for Symbol Recognition
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