Abstract
The spatiostructural features proposed for recognition of online handwritten characters refer to offline-like features that convey information about both the positional and structural (shape) characteristics of the handwriting unit. This paper demonstrates the effectiveness of representing an online handwritten stroke using spatiostructural features, as indicated by its effect on the stroke classification accuracy by a Support Vector Machine (SVM) based classifier. The study has been done on two major Indian writing systems, Devanagari and Tamil. The importance of localization information of the structural features and handling of translational variance is studied using appropriate approaches to zoning the handwritten character.
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Swethalakshmi, H., Sekhar, C.C., Chakravarthy, V.S. (2007). Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_24
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DOI: https://doi.org/10.1007/978-3-540-74695-9_24
Publisher Name: Springer, Berlin, Heidelberg
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