Personalized handwriting recognition via biased regularization

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 realworld data set show that the number of errors can be reduced by over 40% with as few as five user samples per character.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Wolf Kienzle, Kumar Chellapilla
• Keywords: Class Realworld Data, Generic Recognizer Enters, ICML 2006, Machine Learning, Simple Personalization Framework