A perturbation model for generating synthetic text lines from existing cursively handwritten lines of text produced by human writers is presented. Our purpose is to improve the performance of an HMM-based off-line cursive handwriting recognition system by providing it with additional synthetic training data. Two kinds of perturbations are applied, geometrical transformations and thinning/thickening operations. The proposed perturbation model is evaluated under different experimental conditions.
Generation of synthetic training data for an HMM-based handwriting recognition system
01.01.2003
449723 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Generation of Synthetic Training Data for an HMM-Based Handwriting Recognition System
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