Automatic Signature Verification
Signature verification involves distinguishing between authentic signatures and forgeries. Inspired from facial recognition networks, a one-shot learning convolutional neural network was designed to detect forgeries of a given signature.
A Siamese architecture was developed using PyTorch and was trained using triplet loss. A dataset containing several authentic signatures & forged signatures of around 2600 people (around 75,000 samples) was used. The final network converts the input signatures to a lower-dimensional embedding vector for comparison. It is able to identify forgeries even with only one or two true signatures for reference.
This project was done in Summer 2019 as an internship project (as a requirement of the ‘Practice School’ programme of BITS Goa) for Bank of Maharashtra.