Abstract
This paper proposes an efficient method for the identification of forged signatures from offline handmade signatures. Signatures are majorly used as personal verification that can be abused by any unauthorized third party who would feign the identification of an individual which signifies the need for an automatic forgery identification system. Over a past few decades, there are significant attempts for the identification of forgery of signatures. Identification can be done either online- or offline-based mode. Offline mode works on the input image of a sign. We aim to purpose a technique for offline identification by using a simple shape based on geometrical features including area, center of gravity, eccentricity, kurtosis. Database of signatures is trained for two classes, genuine and forged. Two different classifiers, one based on SVM and other on artificial neural network (ANN), were used to identify and classify the signatures as genuine and forged.
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Agrawal, P., Bhalsodia, R., Garg, Y. (2020). Using Neural Network to Identify Forgery in Offline Signatures. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_63
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DOI: https://doi.org/10.1007/978-981-15-1286-5_63
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