ISSN 0253-2778

CN 34-1054/N

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Recognition of ancient Chinese characters based on hybrid kernel WLS-SVR

  • 1.

    School of Electronics and Information Engineering, Anhui University, Hefei 230601, China

  • 2.

    Editorial Department of Anhui University, Hefei 230039, China

Funds:  Supported by the National Natural Science Foundation of China (61172127), Natural Science Foundation of Anhui Province (1408085MF121).
Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.04.010
More Information
  • Corresponding author: Hu Gensheng(corresponding author), male, born in 1971. PhD/ associate professor. Research field: Machine learning, remote sensing image processing and intelligent algorithm.E-mail：hugs2906@sina.com
  • Received Date: 10 June 2014
  • Accepted Date: 29 December 2014
  • Rev Recd Date: 29 December 2014
  • Publish Date: 30 April 2015
    Abstract

  • Abstract

    The shapes of ancient Chinese characters are often uncertain, which reduces the accuracy of recognition by many classifiers. To solve this problem, a new recognition algorithm combining adaptive weighted least squares support vector regression(WLS-SVR) with hybrid kernel function was proposed to recognize ancient Chinese characters. The weight coefficients of WLS-SVR decayed at a rate of the exponential function of prediction errors. The hybrid kernel was constructed using the wavelet kernel function with local properties and RBF kernel function with global properties. For feature extraction, global point density and component structure are fused with local features of pseudo 2D elastic mesh and local point density. Experiment results show the good robustness and high recognition accuracy of the proposed method.

    Abstract

    The shapes of ancient Chinese characters are often uncertain, which reduces the accuracy of recognition by many classifiers. To solve this problem, a new recognition algorithm combining adaptive weighted least squares support vector regression(WLS-SVR) with hybrid kernel function was proposed to recognize ancient Chinese characters. The weight coefficients of WLS-SVR decayed at a rate of the exponential function of prediction errors. The hybrid kernel was constructed using the wavelet kernel function with local properties and RBF kernel function with global properties. For feature extraction, global point density and component structure are fused with local features of pseudo 2D elastic mesh and local point density. Experiment results show the good robustness and high recognition accuracy of the proposed method.
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    • References(17)
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    Zhang P. Research of digital construction of Paper files and relics [J]. Cultural Relics of Central Plains, 2009(5):104-107.
    [2]
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    Chen D, Li N, Li L. Online handwriting recognition research of ancient character[J]. Journal of Beijing Institute of Mechanical Industry, 2008(4):32-37.
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    Wang L G , Liu D F , Wang Q M et al. Spectral Unmixing Model Based on Least Squares Support Vector Machine With Unmixing Residue Constraints[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1592-1596.
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    Liu B Y, Yang R G. A novel method based on PCA and LS-SVM for power load forecasting[C]. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, NanJing, 2008: 759-763.
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    Zhang H R, Wang X D, Zhang C J et al. Soft sensor technique using LS-SVM and standard SVM[J]. IEEE International Conference on Information Acquisition, Hong Kong and Macau, 2005: 124-127.
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    Xie J H. Printed character recognition using Kernel CCA with LS-SVM method[C]. Computer and Automation Engineering, 2010: 284-287.
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    Yin D Y, Wu Y Q. Detection of Small Target in Infrared Image Based on KFCM and LS-SVM[C].International Conference on Intelligent Human-Machine Systems and Cybernetics, 2010: 309-312.
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    Suykens J A K, de Brabanter J, Lukas L, Vandeewalle J. Weighted Least squares support vector machines: robustness and sparse approximation[J]. Neurocomputing, 2002(48):85-105.
    [14]
    Smits G F, Jordaan E M. Improved SVM regression using mixtures of kernels[C]. textitin Neural Networks, International Joint Conference on, 2002, 3: 2785-2790.
    [15]
    温昌兵. 基于特征融合的脱机手写体汉字识别[D]. 北京，北京科技大学，2005.
    [16]
    Zhang X B, Huang H, Zhang S J. A FCM clustering algorithm based on Semi-supervised and Point Density Weighted[C]. Intelligent Computing and Intelligent Systems, 2010: 710-713.
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    Tu Y K, Chen Q H, Huang L. Handwritten Chinese character recognition based on pseudo two-dimensional elastic mesh [J]. Journal of Huazhong University of Science and Technology, 2010(38):38-40.
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    [1]
    Zhang P. Research of digital construction of Paper files and relics [J]. Cultural Relics of Central Plains, 2009(5):104-107.
    [2]
    Lv X Q, Li M N et al. An oracle classification method based on figure recognition [J]. Journal of Beijing University of Information Science and Technology, 2010(25):92-96.
    [3]
    Chen D, Li N, Li L. Online handwriting recognition research of ancient character[J]. Journal of Beijing Institute of Mechanical Industry, 2008(4):32-37.
    [4]
    Zang G Q. Experiment and Improvement of accuracy of OCR for text-digital image [J]. Intelligence, Information and Sharing, 2010(3):62-67.
    [5]
    Vapnik V. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.
    [6]
    Suykens J A K, Vandewalle J. Least Squares Support Vector Machine Classifiers[J]. Neural Process Letters, 1999 (3):293-300.
    [7]
    Miranian A, Abdollahzade M. Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(2): 207-218.
    [8]
    Wang L G , Liu D F , Wang Q M et al. Spectral Unmixing Model Based on Least Squares Support Vector Machine With Unmixing Residue Constraints[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1592-1596.
    [9]
    Liu B Y, Yang R G. A novel method based on PCA and LS-SVM for power load forecasting[C]. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, NanJing, 2008: 759-763.
    [10]
    Zhang H R, Wang X D, Zhang C J et al. Soft sensor technique using LS-SVM and standard SVM[J]. IEEE International Conference on Information Acquisition, Hong Kong and Macau, 2005: 124-127.
    [11]
    Xie J H. Printed character recognition using Kernel CCA with LS-SVM method[C]. Computer and Automation Engineering, 2010: 284-287.
    [12]
    Yin D Y, Wu Y Q. Detection of Small Target in Infrared Image Based on KFCM and LS-SVM[C].International Conference on Intelligent Human-Machine Systems and Cybernetics, 2010: 309-312.
    [13]
    Suykens J A K, de Brabanter J, Lukas L, Vandeewalle J. Weighted Least squares support vector machines: robustness and sparse approximation[J]. Neurocomputing, 2002(48):85-105.
    [14]
    Smits G F, Jordaan E M. Improved SVM regression using mixtures of kernels[C]. textitin Neural Networks, International Joint Conference on, 2002, 3: 2785-2790.
    [15]
    温昌兵. 基于特征融合的脱机手写体汉字识别[D]. 北京，北京科技大学，2005.
    [16]
    Zhang X B, Huang H, Zhang S J. A FCM clustering algorithm based on Semi-supervised and Point Density Weighted[C]. Intelligent Computing and Intelligent Systems, 2010: 710-713.
    [17]
    Tu Y K, Chen Q H, Huang L. Handwritten Chinese character recognition based on pseudo two-dimensional elastic mesh [J]. Journal of Huazhong University of Science and Technology, 2010(38):38-40.
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