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An MLP-orthogonal Gaussian mixture model hybrid model for Chinese bank check printed numeral recognition

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Abstract.

A hybrid model based on the combination of an orthogonal Gaussian mixture model (OGMM) and a multilayer perceptron (MLP) is proposed in this paper that is to be used for Chinese bank check machine printed numeral recognition. The combination of MLP with OGMM produces a hybrid model with high recognition accuracy as well as an excellent outlier rejection ability. Experimental results show that the proposed model can satisfy the requirements of Chinese bank check printed numeral recognition where high recognition accuracy, high processing speed, and high reliability are needed.

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Authors and Affiliations

  1. Pattern Recognition Research Center, Harbin Institute of Technology, P.O. Box 352, 150001, Harbin, P.R. China

    Hui Zhu, X. L. Tang & Peng Liu

Authors
  1. Hui Zhu
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  2. X. L. Tang
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  3. Peng Liu
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Correspondence to Hui Zhu.

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Correspondence to: Hui Zhu

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Zhu, H., Tang, X.L. & Liu, P. An MLP-orthogonal Gaussian mixture model hybrid model for Chinese bank check printed numeral recognition. IJDAR 8, 27–34 (2006). https://doi.org/10.1007/s10032-005-0143-x

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  • DOI: https://doi.org/10.1007/s10032-005-0143-x

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