The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Techniques for Automatic Clustering of Handwritten Characters
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Principal Component Analysis for Online Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
COACH: cumulative online algorithm for classification of handwriting deficiencies
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
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We describe a method for identifying different writing styles of online handwritten characters based on clustering. The motivation of this experiment is to develop automatic characterization of different writing styles that arise due to variation in stroke number or stroke ordering. An ef- ficient agglomerative hierarchical clustering technique with the nearest neighbor approach was implemented to cluster strokes. The results obtained from our experiment indicate that the resulting prototypes are unique and essentially capture different writing styles.