Online writer verification using kanji handwriting

Authors:
Yoshikazu Nakamura;Masatsugu Kidode
Affiliations:
Nara National College of Technology, Nara, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

Citing 6
Cited 2

Reliable On-Line Human Signature Verification Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM On-line Signature Verifier Incorporating Signature Trajectories

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Study of the Consistency of Some Discriminatory Features Used by Document Examiners in the Analysis of Handwritten Letter a'

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Individuality of Handwritten Characters

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Individuality Analysis of Online Kanji Handwriting

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition

Combined handwriting and speech modalities for user authentication

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Modelling combined handwriting and speech modalities

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates writer verification using handwritten kanji characters on a digitizing tablet. Features representing individuality, which are derived from the knowledge of document examiners, are automatically extracted and then the features effective in writer verification are selected from the extracted features. Two classifiers based on frequency distribution of deviations of the selected features are proposed and evaluated by verification experiments. The experimental results show that the proposal methods are effective in writer verification.