Handwritten recognition with multiple classifiers for restricted lexicon

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Handwritten recognition with multiple classifiers for restricted lexicon

De Oliveira, J. J. and Kapp, M. N. and Freitas, C. O.De A. and De Carvalho, J. M. and Sabourin, R.

Brazilian Symposium of Computer Graphic and Image Processing 2004

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. © 2004 IEEE.

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