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Recognition of Signature Using Neural Network and Euclidean Distance for Bank Cheque Automation

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

Abstract

Handwritten signature recognition plays significant role in automatic document verification system in particularly bank cheque authorization. The proposed method focuses on A novel technique for offline signature recognition approach for bank cheque based on zonal features and regional features. These combined features are used to find genuinety of signature using Euclidean distance as a metric. Extensive experiments are carried out to exhibit the success of the recommended approach.

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Author information

Authors and Affiliations

  1. NES Research Foundation, Department of MCA, JNNCE, Shimoga, Karnataka, India

    S. P. Raghavendra

  2. Department of Computer Science and Engineering, Christ (Deemed to be university), Bangalore, Karnataka, India

    Ajit Danti

Authors
  1. S. P. Raghavendra
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  2. Ajit Danti
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Corresponding author

Correspondence to S. P. Raghavendra .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Raghavendra, S.P., Danti, A. (2019). Recognition of Signature Using Neural Network and Euclidean Distance for Bank Cheque Automation. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_21

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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