Indexation of Document Images Using Frequent Items

Eugen Barbu, Pierre Heroux, Sebastien Adam, Eric Trupin

Abstract

Documents exist in different formats. When we have document images, in order to access some part, preferably all, of the information contained in that images, we have to deploy a document image analysis application. Document images can be mostly textual or mostly graphical. If, for a user, a task is to retrieve document images, relevant to a query from a set, we must use indexing techniques. The documents and the query are translated in a common representation. Using a dissimilarity measure (between the query and the document representations) and a method to speed-up the search process we may find documents that are from the user point of view relevant to his query. The semantic gap between a document representation and the user implicit representation can lead to unsatisfactory results. If we want to access objects from document images that are relevant to the document semantic we must enter in a document understanding cycle. Understanding document images is made in systems that are (usually) domain dependent, and that are not applicable in general cases (textual and graphical document classes). In this paper we present a method to describe and then to index document images using frequently occurences of items. The intuition is that frequent items represents symbols in a certain domain and this document description can be related to the domain knowledge (in an unsupervised manner). The novelty of our method consists in using graph summaries as a description for document images. In our approach we use a bag (multiset) of graphs as description for document images. From the document images we extract a graph based representation. In these graphs, we apply graph mining techniques in order to find frequent and maximally subgraphs. For each document image we construct a bag with all frequent subgraphs found in the graph-based representation. This bag of “symbols” represents the description of the document.

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Paper Citation


in Harvard Style

Barbu E., Heroux P., Adam S. and Trupin E. (2005). Indexation of Document Images Using Frequent Items . In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005) ISBN 972-8865-28-7, pages 164-174. DOI: 10.5220/0002576001640174


in Bibtex Style

@conference{pris05,
author={Eugen Barbu and Pierre Heroux and Sebastien Adam and Eric Trupin},
title={Indexation of Document Images Using Frequent Items},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)},
year={2005},
pages={164-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002576001640174},
isbn={972-8865-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)
TI - Indexation of Document Images Using Frequent Items
SN - 972-8865-28-7
AU - Barbu E.
AU - Heroux P.
AU - Adam S.
AU - Trupin E.
PY - 2005
SP - 164
EP - 174
DO - 10.5220/0002576001640174