Writer adaptation of online handwriting models
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Writer adaptation is the process of converting a writer-independent handwriting recognition system, which models the characteristics of a large group of writers, into a writer-dependent system, which is tuned for a particular writer. Adaptation has the potential of increasing recognition accuracies, provided adequate models can be constructed for a particular writer. The limited amount of data that a writer typically provides makes the role of writer-independent models crucial in the adaptation process. Our approach to writer-adaptation makes use of writer-independent writing style models (called lexemes), to identify the styles present in a particular writer's training data. These models are then retrained using the writer's data. We demonstrate the feasibility of this approach using hidden Markov models trained on a combination of discretely and cursively written lower case characters. Our results show an average reduction in error rate of 16.3% for lower case characters as compared against representing each of the writer's character classes with a single model.Keywords
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