Prototype Learning Methods for Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
An Approach to Identify Unique Styles in Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
On-line handwritten digit recognition based on trajectory and velocity modeling
Pattern Recognition Letters
Finding Arbitrary Shaped Clusters for Character Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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This work reports experiments with four hierarchical clustering algorithms and two clustering indices for on-line handwritten characters. The main motivation of the work is to develop an automatic method for finding a set of prototypical characters which would represent well the different writing styles present in a large international database. One of the major obstacles in achieving this goal is the uneven representation of different writing styles in the database. On the basis of the results of the experiments, we claim that a good set of prototypes can be formed from the combined results of the different clustering algorithms. However, the number of clusters cannot be determined automatically but some human intervention is required.