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

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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.

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Dutch Data is an open-source, freely available data set.

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Acknowledgements

The authors express their profound gratitude to Mr. Anup Kumar Mahapatra (Deputy Managing Director and Chief Information Officer, Global IT Center, The State Bank of India), Mr. Kunjal Prasad (former General Manager, Data and Analytics, Global IT Center, The State Bank of India), Mr. NDSV Nageswara Rao (Erstwhile Deputy General Manager, Analytics, Global IT Center, The State Bank of India), Mr. Kamal Kishor Naik (Chief Manager, Analytics, Global IT Center, The State Bank of India)), Mr. Komaragiri Srinivas Jagannath (Deputy Manager, Analytics, Global IT Center, The State Bank of India) and Ms. Vatsala Sinha(Intern) for their valuable contributions, feedback, and suggestions. We are also very thankful to all volunteers who provided signatures and helped create the dataset used for model building.

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Authors and Affiliations

  1. Analytics Department, Global I.T Centre, SBI, Navi Mumbai, India

    Prashant Singh, Prashant Verma & Nikhil Singh

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  1. Prashant Singh
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  2. Prashant Verma
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  3. Nikhil Singh
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Contributions

PS proposed the problem, analyzed the data, build the models, and did the initial literature writing. Dr. PV designed the study, reviewed the literature, revised the manuscript critically for important intellectual content, and gave final approval of the version to be published. NS Conceptualized the problem, provided his inputs for model building and performed the relevant statistical analysis, did model validation, and reviewed the literature. All authors read and approved the final manuscript.

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Correspondence to Prashant Verma.

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Singh, P., Verma, P. & Singh, N. Offline Signature Verification: An Application of GLCM Features in Machine Learning. Ann. Data. Sci. 9, 1309–1321 (2022). https://doi.org/10.1007/s40745-021-00343-y

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  • DOI: https://doi.org/10.1007/s40745-021-00343-y

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