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Generation of Diversiform Characters Using a Computational Handwriting Model and a Genetic Algorithm

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Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

In pattern recognition, a diversification of characters is necessary for learning, which is like neural network learning. The artificial diversification of characters has been suggested as one means of collecting a variety of characters. Accordingly, we show that a computational handwriting model can be applied to the diversification of characters. It is thought that characters diversified by the model can be used as a database of character images for learning. Wada & Kawato’s handwriting model [11] is based on an optimal principle and the feature space of the characters includes sets of via-points extracted from actual handwritten characters. Therefore, if the via-point information is changed, diversiform characters can be generated using the handwriting model. In this paper, we propose a method for generating a diversification of characters by changing via-point information based on a genetic algorithm.

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Authors and Affiliations

  1. Nagaoka University of Technology, Nagaoka, 940-2188, Japan

    Yasuhiro Wada, Kei Ohkawa & Keiichi Sumita

Authors
  1. Yasuhiro Wada
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  2. Kei Ohkawa
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  3. Keiichi Sumita
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Wada, Y., Ohkawa, K., Sumita, K. (2001). Generation of Diversiform Characters Using a Computational Handwriting Model and a Genetic Algorithm. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_170

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  • DOI: https://doi.org/10.1007/3-540-44668-0_170

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

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