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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Improving Isolated Handwritten Word Recognition Using a Specialized Classifier for Short Words
verfasst von : Francisco Zamora-Martínez, María José Castro-Bleda, Salvador España-Boquera, Jorge Gorbe
Erschienen in: Current Topics in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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The aim of this work is to improve the performance of off-line handwritten text recognition systems based on hidden Markov models (HMM) and hybrid Markov models with neural networks (HMM/ANN). In order to study the systems without the influence of the language model, an isolated word recognition task has been performed. The analysis of the influence of word lengths on the error rates of the recognizers has lead to combine those classifiers with another one specialized in short words. To this end, various multilayer perceptrons have been trained to classify a subset of the vocabulary in a holistic manner. Combining the classifiers by means of a variation of the Borda count voting method achieves very satisfying results.