Handwritten Word Recognition Using Classifier Ensembles Generated From Multiple Prototypes

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

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