Computer Graphics and Image Processing, XVII Brazilian Symposium on (SIBGRAPI'04) Handwritten Recognition with Multiple Classifiers for Restricted Lexicon Curitiba, PR, Brazil October 17-October 20 ISBN: 0-7695-2227-0
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.
Citation:
J. J. de Oliveira Jr., M. N. Kapp, C. O. de A. Freitas, J. M. de Carvalho, R. Sabourin, "Handwritten Recognition with Multiple Classifiers for Restricted Lexicon," sibgrapi, pp.82-89, Computer Graphics and Image Processing, XVII Brazilian Symposium on (SIBGRAPI'04), 2004 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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