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Handwritten Numeral Recognition Using Support Vector Machine with Bias

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2011 International Conference in Electrics, Communication and Automatic Control Proceedings

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Abstract

Handwriting recognition has always been one of the most challenging tasks in the field of image processing and pattern recognition. As a new classification method based on learning, Support Vector Machine (SVM) is now actively studied and applied widely in pattern recognition problems. In this chapter, we propose an algorithm called SVM with bias, then the proposed algorithm is applied to recognizing handwritten numerals. The experiments on the United States Postal Service (USPS) numeral database demonstrate the effectiveness of the approach.

Sponsored by National Natural Science Foundation of China (51004005) and Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR201107123).

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Author information

Authors and Affiliations

  1. School of Science, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China

    Zhijie Xu

  2. School of Surveying and Mapping Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China

    Jianqin Zhang

  3. Basic Science Department, Harbin Commercial University, 150028, Harbin, China

    Zhidan Xu

  4. School of Mechanical-Electronic and Automobile Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China

    Zhigang Chen

Authors
  1. Zhijie Xu
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  2. Jianqin Zhang
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  3. Zhidan Xu
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  4. Zhigang Chen
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Corresponding author

Correspondence to Zhijie Xu .

Editor information

Editors and Affiliations

  1. Test, Chongqing University of Technology, The Key Laboratory of Manufacture and, Shapingba Three Gorges square Lihai building 14-C1, Chongqing, 400030, China, People's Republic

    Ran Chen

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Xu, Z., Zhang, J., Xu, Z., Chen, Z. (2012). Handwritten Numeral Recognition Using Support Vector Machine with Bias. In: Chen, R. (eds) 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8849-2_136

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  • DOI: https://doi.org/10.1007/978-1-4419-8849-2_136

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-8848-5

  • Online ISBN: 978-1-4419-8849-2

  • eBook Packages: EngineeringEngineering (R0)

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