You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Offline Handwritten Signature Verification - Literature Review 
Chapter Title:  Neural Networks 
1. Abstract of Cited Paper (BIB001):  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.

Literature Review:
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.