Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1143844.1143902 | ICML |
Keywords | Field | DocType |
adaptation step,user sample,personalized handwriting recognition,generic recognizer,improved recognition rate,particular user,new information,new approach,modified svm,modified regularization term,handwriting recognition | Pattern recognition,Intelligent character recognition,Computer science,Support vector machine,Handwriting recognition,Speech recognition,Regularization (mathematics),Artificial intelligence,Fuse (electrical),Machine learning,Personalization | Conference |
ISBN | Citations | PageRank |
1-59593-383-2 | 34 | 1.57 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wolf Kienzle | 1 | 391 | 20.73 |
| Kumar Chellapilla | 2 | 951 | 62.13 |