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A new Chinese character recognition approach based on the fuzzy clustering analysis

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Abstract

In this paper, a new Chinese character recognition (CCR) approach is proposed based on the fuzzy clustering analysis theory. Chinese characters (CCs) have various similar radicals and stroke components, which make it difficult to recognize features in the CCR process. At the same time, the recognition accuracy and the efficiency are lower when the objects to be recognized are complex. In order to solve these problems, a fuzzy clustering analysis method is introduced to enhance the computing efficiency. At first, the CCs including learning samples and testing samples are transformed into binarization templates in the form of matrixes. Then, the minimum distance algorithm is applied to calculate ‘distances’ between the testing sample templates and the learning sample templates. At last, the character recognition can be achieved by searching the minimum distance from the results. The experiment results of the CCR process can prove the effectiveness and accuracy of the new method.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou, 221116, People’s Republic of China

    W. Y. Liu & J. L. Jiang

  2. Department of Mechanical and Aerospace Engineering, Case School of Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    W. Y. Liu

Authors
  1. W. Y. Liu
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  2. J. L. Jiang
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Correspondence to W. Y. Liu.

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Liu, W.Y., Jiang, J.L. A new Chinese character recognition approach based on the fuzzy clustering analysis. Neural Comput & Applic 25, 421–428 (2014). https://doi.org/10.1007/s00521-013-1513-9

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  • DOI: https://doi.org/10.1007/s00521-013-1513-9

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