Neural Network Based Offline Signature Recognition and Verification System
Paigwar Shikha, Shukla Shailja · 2013
Handwritten signatures are the most natural way of authenticating a person’s identity. An offline signature verification system generally consists of four components: data acquisition, pre- processing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), modular neural networks with generalized feed-forward networks and Self Organizing Map groups (SOM) neural network with competitive learning will be considered. Self Organizing Map groups the input data into clusters which are commonly used for unsupervised training. After recognition and verification of input data FRR, FAR and TER is calculated.