Offline Handwritten Signature Verification - Literature Review <s> Neural Networks <s> In this paper a method for off-line signature verification based on geometric feature extraction and neural network classification is proposed. The role of signature shape description and shape similarity measure is discussed in the context of signature recognition and verification. Geometric features of input signature image are simultaneously examined under several scales by a neural network classifier. An overall match rating is generated by combining the outputs at each scale. Artificially generated genuine and forgery samples from enrollment reference signatures are used to train the network, which allows definite training control and at the same time significantly reduces the number of enrollment samples required to achieve a good performance. Experiments show that 90% correct classification rate can be achieved on a database of over 3000 signature images. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Neural Networks <s> In this paper, we present a grid structured morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases: preprocessing, feature extraction and verification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical grids and grid structured morphological pattern spectra for each grid is obtained. The grid structured morphological spectrum is represented in the form of 10-bin histogram and normalised to overcome the problem of scaling. The eighty dimensional feature vector is obtained by concatenating all the eight vertical morphological spectrum based normalised histogram. For verification purpose, we have considered two well known classifiers, namely SVM and MLP and conducted experiments on standard signature datasets namely CEDAR, GPDS-160 and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach. <s> BIB002
Neural Networks have been explored by some authors to perform signature verification, in particular for writer-dependent classification. In this case, a dataset for each user is created using genuine samples, and some type of forgery (usually random forgeries, by using genuine samples from other users). Features are extracted using any feature extraction mechanism, and this dataset is then used to train a neural network. Huang and Yan BIB001 used Neural Networks to classify between genuine signatures and random and targeted forgeries. They trained multiple networks on features extracted at different resolutions (grid sizes), and another network to make a decision, based on the outputs Figure 12 : The neural network used by Huang and Yan BIB001 of these networks (see Figure 12 ). Shekar et al BIB002 trained neural networks and support vector machines obtaining state of the art results on the GPDS dataset.