Collaboration between statistical and structural approaches for old handwritten characters recognition

Authors:
Denis Arrivault;Noël Richard;Christine Fernandez-Maloigne;Philippe Bouyer
Affiliations:
Laboratoire SIC – CNRS – FRE 2731, SP2MI, Futuroscope Cedex;Laboratoire SIC – CNRS – FRE 2731, SP2MI, Futuroscope Cedex;Laboratoire SIC – CNRS – FRE 2731, SP2MI, Futuroscope Cedex;RC-SOFT, Domaine de la Combe, Saint-Yriex
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

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Abstract

In this article we try to make different kinds of information cooperate in a characters recognition system addressing old Greek and Egyptians documents. We first use a statistical approach based on classical shape descriptors (Zernike, Fourier). Then we use a structural classification method with an attributed graph description of characters and a random graph modeling of classes. The hypothesis, that structural methods bring topological information that statistical methods do not, is validated on Greek characters. A cooperation with a chain of classifiers based on reject management is then proposed. Due to computation cost, the goal of such a chain is to use the structural approach only if the statistical one fails.