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Offline Signature Verification: An Application of GLCM Features in Machine Learning

Author

Listed:
  • Prashant Singh

    (Global I.T Centre, SBI)

  • Prashant Verma

    (Global I.T Centre, SBI)

  • Nikhil Singh

    (Global I.T Centre, SBI)

Abstract

Signatures are a crucial behavioral trait widely used to authenticate a person's identity. Financial and legal institutions, including commercial banks, consider it a legitimate method of document authentication. Despite the emergence of various biometric authentication techniques such as fingerprints, retinal scans, and facial recognition, signature verification is still a prevalent authentication method among Indian industries, especially in the banking sector. Signature verification is used while processing cheques and other documents, even when only digital copies of such documents are available. An example of signature verification on digital documents is the Cheque Truncation System of India, adopted by all scheduled commercial banks in India. However, manual signature verification is tedious and vulnerable to human error. This paper attempts to compare the efficacy of Convolution Neural Networks and Support Vector Machine algorithms in automating the process of signature verification. These algorithms incorporate various image features to verify whether the signature is genuine or fraudulent without human intervention. The Support Vector Machine algorithm performs better, considering the computational limitations of production systems.

Suggested Citation

  • Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:6:d:10.1007_s40745-021-00343-y
    DOI: 10.1007/s40745-021-00343-y
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    References listed on IDEAS

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    1. Manish Sharma & Shikha N. Khera & Pritam B. Sharma, 2019. "Applicability of Machine Learning in the Measurement of Emotional Intelligence," Annals of Data Science, Springer, vol. 6(1), pages 179-187, March.
    2. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    3. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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