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:  Model Training 
1. Abstract of Cited Paper (BIB001):  Standard signature verification (SV) systems are writer-dependent (WD), where a specific classifier is designed for each individual. It is inconvenient to ask a user to provide enough number of signature samples to design his WD classifier. In practice, very few samples are collected and inaccurate classifiers maybe produced. To overcome this, writer-independent (WI) systems are introduced. A global classifier is designed using a development database, prior to enrolling users to the system. For these systems, signature templates are needed for verification, and the template databases can be compromised. Moreover, state-of-the-art WI and WD systems provide enhanced accuracy through information fusion at either feature, score or decision levels, but they increase computational complexity. In this study, a hybrid WI-WD system is proposed, as a compromise of the two approaches. When a user is enrolled to the system, a WI classifier is used to verify his queries. During operation, user samples are collected and adapt the WI classifier to his signatures. Once adapted, the resulting WD classifier replaces the WI classifier for this user. Simulations on the Brazilian and the GPDS signature databases indicate that the proposed hybrid system provides comparative accuracy as complex WI and WD systems, while decreases the classification complexity. 
2. Abstract of Cited Paper (BIB002):  Signature verification deals with the problem of identifying forged signatures of a user from his/her genuine signatures. The difficulty lies in identifying allowed variations in a user’s signatures, in the presence of high intra-class and low interclass variability (the forgeries may be more similar to a user’s genuine signature, compared to his/her other genuine signatures). The problem can be seen as a nonrigid object matching where classes are very similar. In the field of biometrics, signature is considered a behavioral biometric and the problem possesses further difficulties compared to other modalities (e.g. fingerprints) due to the added issue of skilled forgeries. A novel offline (image-based) signature verification system is proposed in this thesis. In order to capture the signature’s stable parts and alleviate the difficulty of global matching, local features (histogram of oriented gradients, local binary patterns) are used, based on gradient information and neighboring information inside local regions. Discriminative power of extracted features is analyzed using support vector machine (SVM) classifiers and their fusion gave better results compared to state-of-the-art. Scale invariant feature transform (SIFT) matching is also used as a complementary approach. Two different approaches for classifier training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s (genuine) reference signatures from other signatures. On the other hand, a single global SVM trained with difference vectors of query and reference signatures’ features of all users in the training set, learns how to weight the importance of different types of dissimilarities. The fusion of all classifiers achieves a 6.97% equal error rate in skilled forgery tests using the public GPDS-160 signature database. Former versions of the system have won several signature verification competitions such as first place in 4NSigComp2010 and 4NSigComp2012 (the task without disguised signatures); first place in 4NSigComp2011 for Chinese signatures category; first place in SigWiComp2013 for all categories. Obtained results are better than those reported in the literature. One of the major benefits of the proposed method is that user enrollment does not require skilled forgeries of the enrolling user, which is essential for real life applications.

Literature Review:
We now discuss the actual machine learning models used for the task of signature verification. The models can be broadly classified in two groups: writer-dependent and writer-independent. In the first case, that is more common in the literature, a model is trained for each user, using the user's genuine signatures, and commonly random forgeries (by using genuine signature from other users). During the testing phase, a new signature is input to the model, that classifies a sample as genuine or forgery. The writer-independent approach, on the other hand, involves only a single classifier for all users. In this case, usually a distinct set of users is used for training and testing, and during the test phase both the model is used, as well as reference genuine samples for each user, to make a decision (genuine or forgery). Some authors use a combination of both approaches. For example, Eskander et al BIB002 trained a hybrid writer-independent-writer-dependent solution, where a writer-independent classifier is used for classification until a reasonable number of genuine samples for the user is obtained -at this point a writer-dependent classifier is trained and used for subsequent queries. Yilmaz BIB003 propose a hybrid approach, where the results of both a writer-independent and writer-dependent classifiers are combined. As a third scenario, some authors train writer-dependent classifiers, but due to the low number of samples, optimize the models' hyperparameters in a writer-independent format (as in BIB001 ). Besides the most basic classifiers (e.g. simple thresholding and nearest-neighbors), several strategies have been tried for the task of signature verification. The following sections cover the main models used for the task.