Abstract
This paper considers the unsupervised classification of handwritten character strokes in regards to speed, since handwritten strokes prove challenging with their high and variable dimensions for classification problems. Our approach employs a robust feature detection method for brief classification. The dimension is reduced by selecting feature points among all the points within strokes, and thus the need to compare stroke signals between two different dimensions is eliminated. Although there are some remaining problems with misclassification, we safely classify strokes according to handwriting styles through a refinement procedure. This paper illustrates that the equalization problem, the severe difference in small parts between two strokes, can be ignored by summing all of the differences via our method.
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Chang, WD., Shin, J. (2009). Fast Unsupervised Classification for Handwritten Stroke Analysis. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_76
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DOI: https://doi.org/10.1007/978-3-642-01347-8_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01346-1
Online ISBN: 978-3-642-01347-8
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