Abstract
An efficient method for the verification of handwritten signatures using the convolutional neural networks for feature extraction and supervised machine learning techniques is presented. Raw images of signatures are used to train CNN models for extracting features along with data augmentation. CNN architectures used are VGG16, Inception-v3, ResNet50 and Xception. The extracted features are classified into two classes, genuine or forgery using Euclidean distance, cosine similarity and supervised learning algorithm such as Logistic Regression, Random Forest, SVM and its variations. Data used for testing are extracted from ICDAR 2011 Signature Dataset and are organized in pairwise fashion. The database contains signatures of 69 subjects.
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We thank the Indian Institute of Information Technology for constant support and providing opportunity.
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Gupta, Y., Ankit, Kulkarni, S., Jain, P. (2022). Handwritten Signature Verification Using Transfer Learning and Data Augmentation. In: Agarwal, B., Rahman, A., Patnaik, S., Poonia, R.C. (eds) Proceedings of International Conference on Intelligent Cyber-Physical Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7136-4_19
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DOI: https://doi.org/10.1007/978-981-16-7136-4_19
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