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Penmanship Learning Support System: Feature Extraction for Online Handwritten Characters

Abstract : This paper proposes a feature extraction method for online handwritten characters for a penmanship learning support system. This system has a database of model characters. It evaluates the characters a learner writes by comparing them with the model characters. However, if we prepare feature information for every character, information must be input every time a model character is added. Therefore, we propose a method of automatically extracting features from handwritten characters. In this paper, we examine whether it correctly identifies the turns in strokes as features. The resulting extraction rate is 80% and in the remaining 20% of cases, it extracted an area near a turn.
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Tatsuya Yamaguchi, Noriaki Muranaka, Masataka Tokumaru. Penmanship Learning Support System: Feature Extraction for Online Handwritten Characters. 9th International Conference on Entertainment Computing (ICEC), Sep 2010, Seoul, South Korea. pp.496-498, ⟨10.1007/978-3-642-15399-0_71⟩. ⟨hal-01055589⟩



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