Off-line Signature Recognition Research Articles

Advanced Algorithms for Offline Signature Recognition and Enhanced Forgery Detection Mechanisms

In contemporary society, signatures hold significant importance in various critical documents such as bank cheques, passports, and driver's licenses. Unfortunately, they can be counterfeited through various means, giving rise to issues like fraudulent identifications, identity theft, and cyber-attacks. To mitigate this problem, our project is centered around the development of a system that discerns the authenticity of a signature, distinguishing between genuine and forged signatures within a dataset. We have employed Convolutional Neural Networks (CNN) and deep learning for this purpose. This choice is driven by the understanding that signatures evolve over time due to a range of behavioral factors like age, mental state, and physical well-being. Consequently, our system is designed to adapt and learn from diverse training datasets, enhancing its accuracy in detection. While online and offline signature verification methods exist, our project primarily focuses on the latter, specifically targeting the detectionof forged offline signatures.

SIGNATURE FORGERY RECOGNITION

Signatures are one of the most important techniques for biometric authentication. There are two kinds of signature nowadays, offline(static) and dynamic (online). There are greater distinctive characteristics of offline signatures, but there are less distinctive characteristics of offline signatures. Offline signatures are also harder to check. Furthermore, the most significant downside of offline signatures is that even the most talented signer does not sign the same way. This is called Intra-personal variability. Both checking the offline signatures is a difficult problem for researchers. In this research, we proposed an offline signature verification approach based on the Deep Learning to prevent signature fraud by malicious individuals. Index Terms – Offline Signature Recognition, Siamese Neural Network, , Contrastive loss, Euclidean Distance.

Offline signature recognition system using oriented FAST and rotated BRIEF

Despite recent developments in offline signature recognition systems, there is however limited focus on the recognition problem facet of using an inadequate sample size for training that could deliver reliable and easy to use authentication systems. Signature recognition systems are one of the most popular biometric authentication systems. They are regarded as non-invasive, socially accepted, and adequately precise. Research on offline signature recognition systems still has not shown competent results when a limited number of signatures are used. This paper describes our proposed practical offline signature recognition system using the oriented FAST and rotated BRIEF (ORB) feature extraction algorithm. We focus on the practicality of the proposed system, which requires only the minimum number of signatures per user to achieve a high level of fidelity. We manifest the practicality of our approach with a signature database of 300 signatures from 100 different individuals, implying that only two signatures are needed per person to train the proposed system. Our proposed solution achieves a 91% recognition rate with a median matching time of only 7 ms.

Offline Signature Recognition and Verification Using ORBKey Point Matching Techniques

Offline Signature Recognition and Verification Using ORBKey Point Matching Techniques

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.

Review on Offline Signature Recognition and Verification Techniques

Review on Offline Signature Recognition and Verification Techniques

Signature Verification Using Artificial Neural Network

The human signature is most important for access. Signature of the person is important biometric attribute of a human being which is used to authenticate human identity. There are many biometric characteristics by which one can have own identity like face recognition, fingerprint detection, iris inspection and retina scanning. In non-vision based techniques voice recognition and signature verification are most widely used. Verification can be performed either Online or Offline. Online system of signature verification uses dynamic information of a signature captured at the time the signature is made. Offline system uses scanned image of signature. In this paper, I present a method for Offline Verification of signatures using a set of simple shape based geometric features. As signatures play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. This paper presents the off-line signature recognition & verification using neural network in which the human signature is captured and presented in the image format. Various image processing techniques are used to recognize and verify the signature. Preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Initially system use database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used to verify and classify the signatures. The implementation details and results are discussed in the paper.

Writer-independent Offline Signature Recognition based upon Fourier Descriptors

Writer-independent Offline Signature Recognition based upon Fourier Descriptors

An Offline Signature Verification Technique Using Pixels Intensity Levels

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naive Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naive Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naive Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naive Bayes Tree and 88.12% (with forgeries).

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

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.

Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM

which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods. KeywordsSignature verification, offline signature recognition, signatures, SURF features and HMM.

Comparative Analysis of MSER and DTW for Offline Signature Recognition

This paper describes the signature as a one of the most robust and widely used trait in biometric verification. The signature is also known as behavioral biometric property. We present comparative analysis of offline signature recognition based on Dynamic Time Warping (DTW) and Maximally Stable Extremely Regions (MSER). The MSER system reported accuracy of 95.371% where FAR is 5.25% and FRR is 4% whereas DTW system reported accuracy of 70.5% and average horizontal projection WER 70% and average vertical projection WER of 71%.

AN OFFLINE SIGNATURE RECOGNITION AND VERIFICATION SYSTEM BASED ON NEURAL NETWORK

Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.

Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey

Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid processing of documents. Researchers have been proposed various approaches for handwritten signature recognition and verification in the past years. This paper presents a survey for non-western handwritten signature based offline signature verification and identification. In this area, the accuracy rates obtained so far from the available systems is not sufficiently high, and more researches on off-line signature verification as well as off-line signature identification are required.

Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey

Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid processing of documents. Researchers have been proposed various approaches for handwritten signature recognition and verification in the past years. This paper presents a survey for non-western handwritten signature based offline signature verification and identification. In this area, the accuracy rates obtained so far from the available systems is not sufficiently high, and more researches on off-line signature verification as well as off-line signature identification are required.

Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks

Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks

Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks

Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks

Offline Signature Verification Using Pixel Matching Technique

Biometrics (or biometric authentication) refers to the process of identification of humans by their characteristics or traits. Biometrics is used in computer science as a form of identification and access control which is one of the most secure methods to keep humans privacy. Biometric can be classified into two categories: behavioural (signature verification, keystroke dynamics, etc.) and physiological (iris characteristics, fingerprint, etc.). Handwritten signature is amongst the first few biometrics to be used even before the advent of computers. Offline Signature verification is an authentication method that uses the dynamics of a person's handwritten signature measure and analyses the physical activity of signing. The core of a signature biometric system is behavioural, and in this paper we have proposed an off-line signature verification and recognition system using pixel matching technique. PMT (Pixel Matching Technique) is used to verify the signature of the user with the sample signature which is stored in the database. The performance of the proposed method has been compared with the existing ANN (Artificial Neural Network's) back-propagation method and SVM (Support Vector Machine) technique.

Comparative Analysis of Global Feature Extraction Methods for Off-line Signature Recognition

this paper, a method is proposed for feature extraction of offline signature recognition system. The proposed method is based on global features to identify forgeries and also median filter is introduces for noise reduction. The Proposed feature extraction method is compared with Discrete Radon Transform (DRT). Both the feature extraction method extracts one dimensional global features and the alignment between features is performed by Dynamic Time Warping (DTW). When being trained using 6 genuine signatures of each person and 250 forgeries taken from our database, the proposed method obtained an equal error rate (EER) of 8.40%. The false acceptance rate (FAR) for proposed method was also kept as low as 8.80%. Keywordsfilter, Global features, Discrete Radon transform, Dynamic time warping.