In the modern age, technological advancement reached a new limit where authentication plays a vital role in security management. Biometric-based authentication is the most referenced procedure for authentication where signature verification is a significant part of it for authentication of a person. To prevent the falsification of signatures on important documents & legal transactions it is necessary to recognize a person's signature accurately. This paper focused on recognizing offline handwritten original & forged signatures using a deep convolution neural network. We use a completely new dataset & also downloaded datasets to train the system & verify a random signature as genuine or forgery. All testing samples are collected from several individuals after several steps of preprocessing the model is fed with the resultant image to our system, the experimental results give us an accuracy of 95.5% from the dataset.