Handwritten Recognition with Multiple Classifiers for Restricted Lexicon

Authors:
J. J. de Oliveira Jr.;M. N. Kapp;C. O. de A. Freitas;J. M. de Carvalho;R. Sabourin
Affiliations:
UGCG - Universidade Federal de Campina Grande, Brazil;PUCPR - Pontificia Universidade Catolica do Parana, Brazil;PUCPR - Pontificia Universidade Catolica do Parana, Brazil;UGCG - Universidade Federal de Campina Grande, Brazil;ÉTS - Ecole de Technologie Superieure, Canada
Venue:
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Year:
2004

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Abstract

This paper presents a multiple classifier system applied to the handwritten word recognition (HWR) problem. The goal is to analyse the influence of different global classifiers taken isolatedly as well as combined in a particular HWR task. The application proposed is the recognition of the Portuguese handwritten names of the months. The strategy takes advantage of the complementary mechanisms of three different classifiers: Conventional Neural Network, Class-Modular Neural Network and Hidden Markov Models, yielding a multiple classifier that is more efficient than either individual technique. The recognition rates obtained vary from 75.9% using the stand alone HMM classifier to 96.0% considering the classifiers combination.