Authors - Sushant Maji, Sachin B. Jadhav Abstract - The offline signature validation by means of hand written signature is also a significant consideration in the financial, legal and ad- ministrative authentication systems. However, this is particularly challenging because of the inaccessibility of dynamic data of handwriting such as pen-pressure and stroke-velocity, and small training samples. The paper describes a modified version of Siamese-Transformer model called SigNeura, which is also improved with Synthetic Pen Pressure Map Generation to refine the accuracy of the verification in the few-shot learning. The adaptive thresholding, and utilization of the stroke-width estimation is applied to obtain synthetic pressure maps and fill in the dynamic information of the synthetic grayscale signatures with the static grayscale signatures. The Siamese network is optimized on discriminative embeddings and Transformer encoders are optimized on triplet long range contextual dependencies. The analysis conducted on benchmarking data using experiments demonstrates that SigNeura is a significantly superior approach than conventional CNN and Siamese-based approaches with a high level of accuracy and resistance to skilled forgeries.
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