Abstract
Signature-based authentication of human is still very popular approach. Manual checking is not always accurate and it depends on expertise. The need is an automated and accurate system for signature classification. The signatures do not necessarily comprise of well-formed letters. It can be a random combination of curves and lines. The written signature may be of variable sized, inclined in arbitrary angle or misplaced. This makes the classification task more challenging. This paper proposes an automated approach of handwritten signature classification addressing those problems. The binarized version of the input image is pre-processed in various ways to compensate translation, rotation and noise removal. The four features, which does not vary due to scaling, are selected from the pre-processed image for the classification using k-NN classifier. Overall system accuracy of the proposed approach is 92% on a dataset of 100 images.
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Ganguly, C., Jana, S., Parekh, R. (2019). Geometrical Transformation Invariant Approach for Classification of Signatures Using k-NN Classifier. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_9
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DOI: https://doi.org/10.1007/978-981-13-8578-0_9
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