Comparing Adaptation Techniques for On-Line Handwriting Recognition

Authors:
Affiliations:
Venue:
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Year:
2001

Citing 0
Cited 4

On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Systems

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Minimum Classification Error Training for Online Handwriting Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Personalized handwriting recognition via biased regularization

ICML '06 Proceedings of the 23rd international conference on Machine learning
A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: This paper describes an on-line handwriting recognition system with focus on adaptation techniques. Our Hidden Markov Model (HMM)-based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the EM (expectation maximization)-approach or an adaptation according to the MAP (maximum a posteriori) or MLLR (maximum likelihood linear regression)-criterion. The performance of the resulting writer-dependent system increases significantly, even if the amount of adaptation data is very small (about 6 words). So this approach is also applicable for on-line systems in hand-held computers such as PDAs. Special attention was paid to the performance comparison of the different adaptation techniques with the availability of different amounts of adaptation data ranging from a few words up to 100 words per writer.