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Prototype Learning Methods for Online Handwriting Recognition

B.S., Raghavendra; C.K., Narayanan; G., Sita; A.G., Ramakrishnan; M., Sriganesh

External - Copyright Consideration

Keyword(s): handwriting recognition, prototype learning

Abstract: In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise-distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios. Publication Info: ICDAR 2005 International Conference on Document Analysis and Recognition, 29th Aug-1st Sept 2005, Seoul, Korea

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