Handwritten Signature Identification Research Articles

Handwritten Signature Identification Based on Hybrid Features and Machine Learning Algorithms

Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier. Index Terms— Fast Fourier Transform, Gray-Level Co-occurrence Matrix, Handwritten Signature, Linear Discriminant Analysis, Machine Learning.

Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

Handwritten signature identification is the process of identifying the true identity of an individual by analyzing their signature. This is an important task in applications such as financial transactions, legal documents, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (off-line) signature data and proposed deep-based model, this is done by fused three types of signature features, Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, the fused features are inputted to the proposed deep-based model of 25 layers for identifying each person. For experiments, we have used three datasets: Our own private collected dataset, called SigArab, and two public datasets called SigComp11 and CEDAR respectively. The proposed deep model achieves 99.23%, 100%, 100% accuracy on SigArab, CEDAR and SigComp2011 datasets.

Handwritten signature identification based on MobileNets model and support vector machine classifier

Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

Handwritten signature identification based on MobileNets model and support vector machine classifier

Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

Digital image processing: Offline handwritten signature identification using local binary pattern and rotational invariance local binary pattern with learning vector quantization

The Handwritten signature is one of the authentication tools that humans have to determine the authenticity of a file or document. The difference between offline and online handwritten signatures is that offline signatures requires a scanner while online signatures use a stylus. The feature extraction methods used are Local Binary Pattern (LBP), uniform LBP, LBP 8 rotation, and uniform LBP 8 rotation. Learning algorithm used is Learning Vector Quantization (LVQ). This study used 200 images data, derived from 20 participants handwritten signature with contribution of 10 signatures each participants. Training data and test data will be divided based on the k-fold cross validation classification then tested based on 4 groups of data sets with certain parameters. Data set 1 consisted of 5 participants, data set 2 consisted of 10 participants, data set 3 consisted of 15 participants, and data set 4 consisted of 20 participants. The best system performance results with the highest accuracy are in training and testing of data set 1 with maximum of 100 iteration for each method. The results of system accuracy is very affected by total number of participants used, which is the more the number of participants with signatures processed, the accuracy will be reduced. The detailed results of system performances accuracy using data set 1 for the LBP extraction method obtained an accuracy of 84%, uniform LBP obtained an accuracy of 90%, LBP 8 rotation obtained an accuracy of 86%, and LBP 8 uniform rotation obtained an accuracy of 80%.

SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

Handwritten Signature Identification Based on Wavelet Transform and CPN Network

Handwritten Signature Identification Based on Wavelet Transform and CPN Network

Signature handwriting identification based on generative adversarial networks

Handwritten signature has been an important identity-verification method since ancient times. Compared with manual handwriting verification, the use of computer image recognition technology for handwriting verification is faster and avoids subjectivity. However, there are still some challenges in traditional image recognition methods, such as feature selection, lack of a standard basis, and low accuracy. For the first time, generative adversarial nets (GAN) technology is adopted to study the task of handwritten signature identification. A special network SIGAN (Signature Identification GAN, SIGAN)is proposed based on the idea of dual learning. The loss value of the trained discriminator of SIGAN is used as the identification threshold. The authenticity of the test handwritten signature is determined by comparing the threshold and loss value of the test image obtained through the network. The experimental data set in this study consists of five hard pen-type signatures, which include some real signatures and some deliberate imitations. The experimental results show that the average accuracy of the SIGAN-based signature identification model is 91.2%, which is 3.6% higher than that of the traditional image classification method.

An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis

Offline handwritten signature identification has received less attention in comparison with the offline signature verification, despite its crucial applications such as in law-enforcements, automatic bank check and historical documents processing. In this paper, an Open Handwritten Signature Identification System (OHSIS) is proposed by using conjointly the Curvelet Transform (CT) and the One-Class classifier based on Principal Component Analysis (OC-PCA). CT is explored for feature generation due to its efficient characterization of curves contained into the local orientations within the signature image. While, OC-PCA is used for its effectiveness to absorb the high feature size generated by the CT and allows achieving at the same time an open system. Then, in order to improve the robustness of the OHSIS when few reference signatures are available, a new combination approach based on Choquet fuzzy integral is proposed to combine multiple individual OHSISs. Furthermore, a designing protocol with limited number of writers and reference signatures is proposed to perform a parameter-independent OHSIS. Experimental results conducted on standard CEDAR and GPDS handwritten signature datasets report 97.99% and 94.96% correct identification rate, respectively, which highlights the effectiveness of the proposed OHSIS since it can comfortably outperform the state-of-the-art when using few reference signatures.

A SIGNATURE IDENTIFICATION SYSTEM WITH PRINCIPAL COMPONENT ANALYSIS AND STENTIFORD THINNING ALGORITHMS

Several biometric security systems have been implemented. Biometric is the use of a person’s physiological or behavioural characteristics to identify the individual. An example of behavioural method of biometric is signature identification. Signature identification is the use of handwritten signature to identify a person. This paper attempt design and implement an algorithm for handwritten signature identification. The signature identification system consists of signature acquisition, preprocessing, features extraction and matching stages. Signature acquisition can be either online or offline (both were considered in this research work). Online signatures are obtained by signing on digital tablets while offline signatures are scanned (or snapped) into the system. Preprocessing stage of the system include turning the image to greyscale. The grey image is further converted to binary (black and white). The image is then thinned, using Stentiford thinning algorithm. Stentiford thinning algorithm in an iterative thinning method with a good thinned imaged output. The image is finally cropped to rid the image of unnecessary white spaces. For features extraction, principal component analysis is used. Principal Component Analysis is a good statistical tool for identifying pattern in data. Features extracted from each signature are stored as a template. After features extraction, the distance between signature templates are computed using Manhattan distance. If the distance exceeds a certain threshold, the test signature is rejected (otherwise it is accepted). The design system has a FAR of 4% and an FRR of 6% for offline signatures. A FAR of 2% and an FRR of 3% were obtained for online signatures

Arabic Handwritten Signature Identification

This paper proposes a new intelligent off-line Arabic handwritten signature identification and verification system based on texture analysis. The system uses the texture as feature and back propagation neural network as classifier. The signature image is preprocessed by several operations (Noise removal, Conversion of the signature image to binary image, Finding outer rectangle, Thinning and Size normalization) then the fractal number and co-occurrence matrix are computed to estimate texture features. In this work, two off-line Arabic handwritten signature identification systems are constructed. The first one uses the nearest Euclidean distance, while the other uses back propagation neural network. The paper analyzes and compares the results obtained from the two proposed systems to show the robustness level of the proposed intelligence system. Furthermore, the proposed system was tested by using Genuine signatures and has achieved a CCR (Correct Classification Rate) of 100% in best cases, while it was tested by using Forged signatures it has achieved a CRR approximated to 96.3% in best cases. The experimental results showed that the proposed system is efficient and competent with other state-of-the-art texture-based off-line signature identification systems.

Handwritten signature identification using basic concepts of graph theory

Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for a...