A NEW STRATEGY FOR IMPROVING FEATURE SETS IN A DISCRETE HMM­BASED HANDWRITING RECOGNITION SYSTEM

F. GRANDIDIER and R. SABOURIN

CENPARMI, Concordia University, 1455 de Maisonneuve Blvd West, Montréal H3G 1M8, Canada LIVIA, Ecole de Technologie Supérieure, 1100 rue Notre Dame Ouest, Montréal H3C 1K3, Canada E­mail: frede@cenparmi.concordia.ca, sabourin@gpa.etsmtl.ca

C.Y. SUEN

CENPARMI, Concordia University,
1455 de Maisonneuve Blvd West, Montréal H3G 1M8, Canada

M. GILLOUX

RMO, Service de Recherche Technique de La Poste,
10, rue de l'Ile Mabon, BP86334, 44263 Nantes Cedex 2, France

In this paper we introduce a new strategy for improving a discrete HMM­based handwriting recognition system, by integrating several information sources from specialized feature sets. For a given system, the basic idea is to keep the most discriminative features, and to replace the others with new ones obtained from new feature spaces. After evaluating the individual discriminative power of each single feature, the set is divided into two subsets: one containing the discriminative features, and the second the others. Considering feature classes in the non­ discriminative feature subset allows the specialization of new feature sets on specific problems. The application of this strategy to an existing system showed an improvement of 16% in the recognition rate when a lexicon of 1000 city names was used.

In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 113-122.