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:  Introduction 
1. Abstract of Cited Paper (BIB001):  Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. 
2. Abstract of Cited Paper (BIB002):  In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

Literature Review:
Biometrics technology is used nowadays in a wide variety of security applications. The aim of such systems is to recognize a person based on physiological or behavioral traits. In the first case, the recognition is based on measurements of biological traits, such as the fingerprint, face, iris, etc. The later case is concerned with behavioral traits such as voice and the handwritten signature  . Biometric systems can perform two tasks: verification and identification. In the first case, a user of the system claims to be a particular person, and provides the biometric sample. The role of the verification system is to check if the user is indeed who he or she claims to be. In the identification case, a user of the system provides a biometric sample, and the objective is to identify, among all the people enrolled in the system, who the person is. The handwritten signature is a particularly important type of biometric trait, mainly due to its ubiquitous use to verify a person's identity in legal, financial and administrative areas. One of the reasons for its widespread use is that the process to collect handwritten signatures is non-invasive, and people are familiar with the use of signatures in their daily life BIB002 . Signature verification systems aim to automatically discriminate if the biometric sample is indeed of a particular person or not, that is, they are used to classify query signatures as genuine or forgeries. This type of system usually consist of an enrolment phase, where the users of the system provide samples of their signatures, and an operation (or classification) phase, where a user claims the identity of a person and provide a query signature. The system then classifies such query as genuine (confirming the user identity), or as a forgery. In the research of automated signature verification systems, forgeries are often classified in three types: random, simple and skilled (or simulated) forgeries. In the case of random forgeries, the forger has no information about the user or his signature and uses his own signature instead. In this case, the forgery contains a different semantic meaning than the genuine signatures from the user, presenting a very different overall shape. In the case of simple forgeries, the forger has knowledge of the user's name, but not about the user's signature. In this case, the forgery may present more similarities to the genuine signature, in particular for users that sign with their full name, or part of it. In skilled forgeries, the forger has access for both the user's name and signature, and often practices imitating the user's signature. This result in forgeries that have higher resemblance to the genuine signature, and therefore are harder to detect. Depending on the acquisition method, signature verification is divided in two categories: online (dynamic) and offline (static). In the online case, an acquisition device (such as a digitizing table) is used to acquire the user's signature. The data is collected as a sequence S(n), n = 1, ..., N , where S(n) is the signal sampled at time n∆ t , ∆ t being the sampling interval. The signal may contain only the position of the pen, or include additional information such as the pen inclination, pressure, etc. In offline signature verification, the signature is acquired for the system after the writing process is completed. In this case, the signature is represented as a digital image, usually in grayscale format, as a set of points S(x, y), 0 <= x <= H, 0 <= y <= W , where H and W denote the image height and width BIB003 . Over the last few decades, some key survey papers have summarized the advancements in the field, in the late 80's BIB001 , 90's  and 2000's BIB003 . This report reviews the most important techniques and recent advancements on the field of Offline Signature verification. It is organized as follows: we start by formalizing the problem at hand, and list the datasets that are available to evaluate such systems. We then describe the techniques used for each process of the pipeline for training a system: Preprocessing, Feature Extraction and model training, and finally we summarize the recent progress.