Handwritten Signature Verification Research Papers (original) (raw)

Signature is a written depiction of someone’s full name, nickname, or a simple letter that has been used on any desired document as an indication of their identity. It is a particular case of handwriting, which includes flourishes and special characters which are produced by the force of habit. Forensically, the signatures are examined for naturalness in execution and for consistent recurrent options that confirm the established practice pattern of the author and aids in the identification of the author in cases of disputes. Commonly used parameters for the signature analysis by the handwriting experts are the formation of the letters, alignment, embellishment, spacing, pen-lifts, pen-pauses, slant, initial & final strokes, and pressure. In some instances where people make initials instead of full signatures, it becomes challenging for the forensics and handwriting experts to compare such initials with complete signatures. The present work has been focused on extracting the similarities between the initial and the complete signatures of an individual to aid in the identification of an individual in such complicated cases. This study may contribute to the identification of the author in the matter of disputes where initials have been used intentionally or unintentionally.

- by
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- Digital Signature, Signature Verification, Handwritten Signature Verification

The writing of names or signatures has great importance due to its use in authentication, validation, and authorization of documents. Moreover, handwritten signatures present an aura of personality and make an impression on many people. But now, with the emergence of new technologies, a variety of electronic writing media such as digital tablets and pens are being used to produce writings and signatures; and the conventional way to produce the writing using pen and paper is waning. With the changing ways of writing and signature production, the means of producing forged writing or signature are also bound to change. This has brought new challenges for handwriting examiners. In the present study, a comparative analysis of electronically captured signatures with pen-paper signatures has been performed to study the effect of changes in writing media. Signature samples were taken from the same subjects on paper and electronic

- by OP Jasuja and +1
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- Handwritten Signature Verification

OCR has been an active research area since last few decades. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like preprocessing, segmentation, recognition and post processing. The preprocessing stage is a crucial stage for the success of OCR, which mainly deals with noise removal. In the present paper, a modified technique for noise removal named as "K-Algorithm" has been proposed, which has two stages as filtering and binarization. The proposed technique shows improvised results in comparison to median filtering technique.

A novel definition of stability regions and a new method for detecting them from on-line signatures is introduced in this paper. Building upon handwriting generation and motor control studies, the stability regions is defined as the longest similar sequences of strokes between a pair of genuine signatures. The stability regions are then used to select the most stable signatures, as well as to estimate the extent to which these stability regions are encountered in both genuine and simulated (forged) signatures, thus modeling the signing habit of a subject. Experimental results on the SUSig database show that the proposed model can be effectively used for signature verification.

- by Antonio Parziale
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- Computer Science, Digital Signature, Handwriting, Forensic Document Examination

We discuss the dynamics of signatures in the light of recent findings in motor learning, according to which a signature is a highly automated motor task and, as such, it is stored in the brain as both a trajectory plan and a motor plan. We then conjecture that such a stored representation does not necessarily include the entire signature, but can be limited to only parts of it, those that have been learned better and therefore are executed more automatically than others. Because these regions are executed more automatically than others, they are less prone to significant variations depending on the actual writing conditions, and therefore should represent better than other regions the distinctive features of signatures. To support our conjecture, we report and discuss the results of experiments conducted by using an algorithm for finding those regions in the signature ink and eventually using them for automatic signature verification.

In this paper Segmentation is one the most important process which decides the success of character recognition fashion. Segmentation is used to putrefy an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple corridor. With respect to the segmentation of handwritten words into characters it's a critical task because of complexity of structural features and kinds in writing styles. Due to this without segmentation these touching characters, it's delicate to fete the individual characters, hence arises the need for segmentation of touching characters in a word. Then we consider Marathi words and Marathi Numbers for segmentation. The algorithm is use for Segmentation of lines and also characters. The segmented characters are also stores in result variable. First it Separate the lines and also it Separate the characters from the input image. This procedure is repeated till end of train.

A signature is considered as an individual and identifying mark made by a person and is often used to authenticate or identify any document. Therefore it becomes necessary in some matters to ensure that a particular signature is genuine or forged and is challenging when questioned and admitted both or any one of them is a photocopied. This happens when either original copy of signature is lost or not available due to any kind of reasons, for examination and comparison purposes. In the case where original copy of signature is not available, then only its photocopy can be considered as secondary document evidence. It is well known fact that a photocopy cannot reproduce all the details of the original document but in the cases where the original documents are either lost or damaged, then photocopies are considered as the best possible evidence. In the present paper an attempt has been made to examine two cases of the signatures from the non-original documents and reported.

This study was carried out in Istanbul University-Cerrahpasa Institute of Forensic Sciences and Legal Medicine, Document Examination Laboratory in order to determine the changes in signatures over time. Signature is a biometric element consisting of descriptive texts and figures that shows an individual's characteristics. The signature, like handwriting, undergoes some changes over time. These changes may occur due to personal and environmental factors. In order to define such changes, a sample group was determined and their signatures on timesheets for 1st, 7th, 15th, 30th, 90th, 180th and 360th days were examined. It is expected that some changes may occur in second signatures which are signed at the end of the working day. These signatures were evaluated in terms of "complexity", "speed", "pressure", "deviation angle from the baseline", "aspect ratio" and "special marks" variables. When the change in signature characteristics in different time periods were examined; it was observed that "speed" has decreased while "pressure" has increased (4, %40) or both have remained constant (5, %50) for most of the participants. While the "complexity" (7, %70) and "deviation angle from the baseline" (9, %90) of the signatures remained constant, "aspect ratio" of signatures in half of the participants tended to remain constant (5, %50). When the change of the second signature on the same day is also examined, it was observed that the second signatures of four participants had more pressure whereas two participants had less pressure; three participants had a tendency to exaggerate the initial stroke of their signatures; four participants had a tendency to decrease the number of mid characters, six participants had changes in their last figure of their signatures and four participants had similar characters in comparison with first signatures.
This study is a preliminary study for more comprehensive studies that are carried out by expanding the sample group and evaluating many factors such as age, gender and professions together.

The main objectives of this work are to describe the online bus pass generation and ticket booking using QR code. Online bus pass generation is helpful to people who are suffering issues with the present technique for the generation of bus pass and renewal. This project consists of two login pages, one for user registration and the other one for admin. Users need to register by submitting their details through online. Once the registration process is done then a security code called One Time Password (OTP) code will be sent to the user's registered mail. This system is used for ticket generation, bus pass formation and renewing of the bus pass of the users. The user can login with Idno and password to perform the pass booking and renewal. Bus Ticket Checker can scan the users QR code to check the validity of bus pass.

In an anonymous note case, the first step in the investigation was an examination of the cash register receipt upon which the message was written. The fact that the note was written on paper not manufactured for writing created significant features which prevented identification. Because of these distortions and disturbances, a comparative examination of handwriting would have been futile. Further examination of the suspects' actual handwriting (exemplars) was curtailed, saving time and costs for the client.

- by Marriage Proposal
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- Handwritten Signature Verification

To share the methodology of FHE in Chinese

In this paper, we propose new offline signature Identification and Verification based on the contourlet coefficient as the feature extractor and Support Vector Machine (SVM) as the classifier. In projected method, first signature image is normalized based on size. After pre-processing, contourlet coefficients are computed on particular scale and direction using contourlet transform in feature extraction. After feature extraction, all extracted coefficients are feed to a layer of SVM classifiers as feature vector. The number of SVM classifiers is equal to the number of classes. Each SVM classifier determines if the input image belongs to the resultant class or not. Proposed methodology implemented using MATLAB R2009a software tool. The research is on standard GPDS960 English signature database, based on this experiment, we achieve a 94% identification rate.

- by Sanjay Gharde and +1
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- Image Processing, Pattern Recognition, Support Vector Machines, Handwritten Signature Verification
- by Moises Diaz-Cabrera
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- Information Systems, Algorithms, Biometrics, Writing
- by Moises Diaz
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- Biometrics, Handwritten Signature Verification

This research provides a summary of widely used Handwritten Signature Verification based feature selection techniques. Moreover, the focus is on selected best features of signature verification, characterized by the number of features represented for each signature and the aim is to discriminate if a given signature is genuine or a forgery. We
presented how the discussion, on the advantages and drawbacks of feature selection techniques, has been handled by several researchers in the past few decades and the recent advancements in the field.

Making payments online is inherently insecure,
especially those involving credit cards where a handwritten
signature is normally required to be authenticated. This paper
describes a system for enhancing the security of online payments
using automated handwritten signature verification. Our system
combines complementary statistical models to analyse both the
static features of a signature (e.g., shape, slant, size), and its
dynamic features (e.g., velocity, pen-tip pressure, timing) to form
a judgment about the signer’s identity. This approach’s novelty
lies in combining output from existing Neural Network and
Hidden Markov Model based signature verification systems to
improve the robustness of any specific approach used alone. The
system can be used to authenticate signatures for online credit
card payments using an existing model for remote authentication.
The system performs reasonably well and achieves an overall
error rate of 2.1% in the best case.

- by endra rahmawati
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- Handwritten Signature Verification, Stretching, Colour Code

Handwritten signatures are considered as the
most natural method of authenticating a person’s identity
(compared to other biometric and cryptographic forms of
authentication). The learning process inherent in Neural
Networks (NN) can be applied to the process of verifying
handwritten signatures that are electronically captured via
a stylus. This paper presents a method for verifying handwritten
signatures by using a NN architecture. Various static
(e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip
pressure, etc.) signature features are extracted and used to
train the NN. Several Network topologies are tested and
their accuracy is compared. The resulting system performs
reasonably well with an overall error rate of 3:3% being
reported for the best case.

L'articolo illustra alcune delle conclusioni di una ricerca statistica sulle prime cento scritture e le relative misurazioni contenute nell'opera di Moretti "I grandi dalla scrittura". Vengono presentate e discusse le frequenze e le medie di 77 segni grafologici diversi e le correlazioni statisticamente significative tra il segno Curva e gli altri segni.

- by Martin Waidhuba
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- Engineering, Computer Science, Software Engineering, Image Processing

Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric applications. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples.

- by Oscar Miguel-Hurtado and +1
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- Complexity, Biometrics, Handwritten Signature Verification

This paper describes a system for performing
handwritten signature verification using complementary statistical
models. The system analyses both the static features
of a signature (e.g., shape, slant, size), and its dynamic features
(e.g., velocity, pen-tip pressure, timing) to form a judgment
about the signer’s identity. This approach’s novelty
lies in combining output from existing Neural Network and
Hidden Markov Model based signature verification systems
to improve the robustness of any specific approach used
alone. The system performs reasonably well and achieves
an overall error rate of 2:1% in the best case. The results of
several other experiments are also presented including using
less reference signatures, allowing multiple signing attempts,
zero-effort forgery attempts, providing visual feedback, and
signing a password rather than a signature.

In this paper, a new model-based writer identification scheme using histogram symbolic representation approach is proposed. In the proposed scheme, initially, some pre-processing techniques are employed to enhance image quality and extract text-lines from each handwritten document image. For each extracted text-line, a set of 92 features are computed based on analysis of connected component, enclosed region, lower and upper contours, fractal code, and Curvelet. Considering the extracted feature vectors, a histogram is created for each feature of every writer as a histogram-valued symbolic data. This process results in a handwriting style model for each individual that consists of a set of histograms. To evaluate the proposed scheme, two different handwritten datasets written in two different scripts (Kannada as an Indian based script and English) were used. The first dataset contains 228 pages written in Kannada by 57 people. The other one is the dataset used in SigWiComp2013 composed of 330 document pages written in English by 55 individuals. The same criteria used in the SigWiComp2013 were followed in our evaluation strategy. Concerning the Kannada dataset, an F-measure of 92.79% was obtained when 114 documents were used in learning stage and the rest (114) were used for testing. For the SigWiComp2013 dataset an F-measure of 26.67% was obtained that is fairly comparable to the best result reported in the literature.

Biometric security devices are now permeating all
facets of modern society. All manner of items including passports,
driver’s licences and laptops now incorporate some form of
biometric data and/or authentication device. As handwritten
signatures have long been considered the most natural method of
verifying one’s identity, it makes sense that pervasive computing
environments try to capitalise on the use of automated Handwritten
Signature Verification systems (HSV). This paper presents a
HSV system that is based on a Hidden Markov Model (HMM)
approach to representing and verifying the hand signature data.
HMMs are naturally suited to modelling flowing entities such
as signatures and speech. The resulting HSV system performs
reasonably well with an overall error rate of 3.5% being reported
in the best case experimental analysis.

In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person's signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

The problem of automatic signature recognition and verification has been extensively investigated due to the vitality
of this field of research. Handwritten signatures are broadly used in daily life as a secure way for personal identification. In this paper a novel approach is proposed for handwritten signature recognition in an off-line environment based on Weightless Neural Network (WNN) and feature extraction. This type of neural networks (NN) is characterized by its simplicity in design and implementation. Whereas no weights, transfer functions and multipliers are required. Implementing the WNN needs only Random Access Memory (RAM) slices. Moreover, the whole process of training can be accomplished with few numbers of
training samples and by presenting them once to the neural network. Employing the proposed approach in signature recognition area yields promising results with rates of 99.67% and 99.55% for recognition of signatures that the network has trained on and rejection of signatures that the network .has not trained on, respectively.

— In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual's handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark off-line English signature dataset (GPDS-300) and a large dataset (BHSig260) composed of Bangla and Hindi off-line signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

- by Sai Kiran
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- Computer Science, Handwritten Signature Verification

Handwritten Signature Verification (HSV) is a natural and trusted method for user identity verification. HSV can be classified into two main categories: offline and online HSV. Offline systems take handwritten signatures from scanned documents, while online systems use specific hardware (e.g., pen tablets) to register pen movements during the act of signing. Online HSV systems may embed signatures (including the signature dynamics) into digital documents. Unfortunately, during their lifetime documents may be repeatedly printed and scanned, and digital to paper conversions may result in loosing
the signature dynamics. The main contribution of this work is a new HSV system for secure handwritten signing of documents. First, we illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document (both paper and digital documents). Secondly, we show how to embed features
extracted from handwritten signatures within the documents themselves, so that no remote signature database is needed. To accomplish the embedding task, we make use
of 2D barcodes. The main challenge here is to be able to store the signature dynamics within the limited capacity of barcodes. Thirdly, we propose a method for the verification of signature dynamics which is compatible to a wide range of mobile devices so that no special hardware is needed. The main challenge here is to achieve a high verification performance, despite constrains due to the limited computational resources and pressure
accuracy of mobile phones. We address the trade-off between discrimination capabilities of the system and the storage size of the signature model. Towards this end, we report the results of an experimental evaluation of our system on different signature datasets.

- by Marco Querini and +1
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- Handwritten Signature Verification

Shill bidding is where spurious bids are introduced
into an auction to drive up the final price for the
seller, thereby defrauding legitimate bidders. Trevathan and
Read presented an algorithm to detect the presence of shill
bidding in online auctions. The algorithm observes bidding
patterns over a series of auctions, and gives each bidder a
shill score to indicate the likelihood that they are engaging
in shill behaviour. While the algorithm is able to accurately
identify those with suspicious behaviour, it is designed for
the instance where there is only one shill bidder. However,
there are situations where there may be two or more shill
bidders working in collusion with each other. Colluding shill
bidders are able to engage in more sophisticated strategies
that are harder to detect. This paper proposes a method
for detecting colluding shill bidders, which is referred to
as the collusion score. The collusion score, either detects a
colluding group, or forces the colluders to act individually
like a single shill, in which case they are detected by the
shill score algorithm. The collusion score has been tested on
simulated auction data and is able to successfully identify
colluding shill bidders.

- by Angelo Marcelli
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- Computer Science, Digital Signature, Handwriting, Forensic Document Examination

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

- by Prof. Surjeet Dalal
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- Handwritten Signature Verification

The "variation" and "individuality" emphasized in this presentation are very important " language" in the document examination. how to communicate to ensure the person who reads the report or listens to the explanation can clearly receive it and becomes the evidence for the court is crucial.
In the year 2021, I experienced some cases in which it has been found that there are significant differences in the analysis and interpretation of the findings between different examiners. The serious converse conclusions are even affecting the court trial, the rights, and interests of the parties. Despite many researchers in this field working in authenticating individuality uniqueness, this presentation tries to discuss the problem we faced and share what I learned from all the cases.

Handwritten signature is used in various applications on daily basis. Whether one signs a contract, work documents, petition, or wants to approve a check payment, one will use personal signature to do all those things. In this paper we use this daily based biometric characteristic for identification and classification of students' papers and various exam documents used at University of Mostar. In this paper we used OpenCV library as an image processing tool for feature extraction. As regards to classification method, we used Support Vector Machine.

User authentication in the context of a secure transaction needs to be continuously evaluated for the risks associated with the transaction authorization. The situation becomes even more critical when there are regulatory compliance requirements. Need for such systems have grown dramatically with the introduction of smart mobile devices which make it far easier for the user to complete such transaction quickly but with a huge exposure to risk. Biometrics can play a very significant role in addressing such problems as a key indicator of the user identity and thus reducing the risk of fraud. While unimodal biometrics authentication systems are being increasingly experimented by mainstream mobile system manufacturers (e.g., fingerprint in iOS), we explore various opportunities of reducing risk in a multimodal biometrics system. The multimodal system is based on fusion of several biometrics combined with a policy manager. A new biometric modality: chirography which is based on user writing on multi-touch screens using their finger is introduced. Coupling with chirography, we also use two other biometrics: face and voice. Our fusion strategy is based on inter-modality score level fusion that takes into account a voice quality measure. The proposed system has been evaluated on an in-house database that reflects the latest smart mobile devices. On this database, we demonstrate a very high accuracy multi-modal authentication system reaching an EER of 0.1% in an office environment and an EER of 0.5% in challenging noisy environments.

- by Hagai Aronowitz and +2
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- Speaker Recognition, Face Recognition, Biometrics, Handwritten Signature Verification