issues in the area of ”Forensic Signature Verification”. Two main approaches

exist in this field- signature verification and signature identification.

Our efforts focus on offline signature verification - the task of

identifying whether a signature is genuine or forged given a genuine

copy of the signature. Working on offline (static images) is a tougher

task because temporal information which can provide key distinguishing

features is missing. A part of our research focuses on trying to determine

which are the key features which can help us discriminate between

genuine and forged signatures and then developing algorithms which are

able to do so images of known genuine signatures and forgeries.

Another focus area is on uating existing machine learning techniques

against the extracted data sets and making suggestions for the same.-issues in the area of ”Forensic Signature Verification”. Two main approaches

exist in this field- signature verification and signature identification.

Our efforts focus on offline signature verification - the task of

identifying whether a signature is genuine or forged given a genuine

copy of the signature. Working on offline (static images) is a tougher

task because temporal information which can provide key distinguishing

features is missing. A part of our research focuses on trying to determine

which are the key features which can help us discriminate between

genuine and forged signatures and then developing algorithms which are

able to do so　images of known genuine signatures and forgeries.

Another focus area is on　uating existing machine learning techniques

against the extracted data sets and making suggestions for the same.