Abstract
In this work, we have proposed a multi-label classification algorithm for signature images that can be used to solve multiple objectives: i) It can tell the identity of the image. ii) Can interpret the language of the content written in the image. iii) It can also identify whether the given image is the genuine signature of the person or the forged one. This paper has used the pretrained model GoogLeNet, that has been finetuned on the largest signature dataset present (GPDS). GoogLeNet is used to extract the features from the signature images, and these features are fed to the three-layer neural network. The neural network has been used for the classification of the features of the image. The model has been trained or tested against two regional datasets Hindi and Bengali datasets.
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Jain, A., Singh, S.K., Singh, K.P. (2022). Signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_18
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