Signature Verification Solution For BFSI Industry

• Signature verification is a technique used to validate the captured image signature to those signature image stored in records. It compares signatures and checks for authenticity. It generates a confidence score, where in a low confidence score suggest a forgery.
• Machine learning development refers to techniques used wherein intelligence is acquired by referring to examples. In order to solve the signature verification problem, learning Signature Verification Solution can be acquired in two ways:

• Person-independent / general learning: it involves learning from a set of genuine and forgeries from a general population and differences between genuine and forged signatures across all individuals are learnt.
• Person-dependent / special learning: it involves learning from multiple samples of only that person’s signatures where in within-person similarities are learnt.

It is observed that special learning performs better than general learning and there is an increase of up to 5% in the accuracy. Hence, we have developed our solution using special learning technique.

In order to perform automatic signature verification there are two parts :

• • Extract features from a signature
  • Determine similarities of two signatures based on these features

Based on the above methods, a confidence score is derived that indicates whether the question signature matched to that particular person or not.

Various scenarios like join accounts where more than one person is allowed to sign on the cheques, can be handled as well with custom logic.

In order to automate this process to an extent, a scanned copy of cheque will be obtained, machine will be able to recognize the account number and signature portion and carry on these steps seamlessly with existing systems.

Accuracy in the range of 80-85% was achieved.