文件名称:SignatureVerificationPaper
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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.
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.
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SignatureVerificationPaper.pdf