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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 312048.0, MPI für biologische Kybernetik / Biologische Kybernetik
Personalized Handwriting Recognition via Biased
Regularization
Authors:Kienzle, W.; Chellapilla, K.
Date of Publication (YYYY-MM-DD):2006-03
Title of Proceedings:International Conference on Machine Learning
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We present a new approach to personalized handwriting recognition.
The problem, also known as writer adaptation, consists of converting
a generic (user-independent) recognizer into a personalized
(user-dependent) one, which has an improved recognition rate for a
particular user. The adaptation step usually involves user-specific
samples, which leads to the fundamental question of how to fuse this
new information with that captured by the generic recognizer. We
propose adapting the recognizer by minimizing a regularized risk
functional (a modified SVM) where the prior knowledge from the
generic recognizer enters through a modified regularization term.
The result is a simple personalization framework with very good
practical properties. Experiments on a 100 class real-world data set
show that the number of errors can be reduced by over 40% with as
few as five user samples per character.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:3928
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