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Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioral features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc. suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a approach for signature verification. We give various approaches that have been proposed for signature verification. Our approach uses Reinforcement machine learning for signature verification.